From a480f0c693823f417efad906e4a950cf6f78f736 Mon Sep 17 00:00:00 2001 From: SteveDiamond Date: Tue, 5 Nov 2024 23:15:20 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20cvxpy/cv?= =?UTF-8?q?xpy@bc2f0aacd9d925d597d217f161264df1a5a74df5=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../scs/finance.CVaRBenchmark.time_compile_problem.json | 1 + ...nance.FactorCovarianceModel.time_compile_problem.json | 1 + .../scs/gini_portfolio.Cajas.time_compile_problem.json | 1 + .../scs/gini_portfolio.Murray.time_compile_problem.json | 1 + .../gini_portfolio.Yitzhaki.time_compile_problem.json | 1 + ...plasticity.ConvexPlasticity.time_compile_problem.json | 1 + ..._regression.HuberRegression.time_compile_problem.json | 1 + ...ing.ConeMatrixStuffingBench.time_compile_problem.json | 1 + ...ing.ParamConeMatrixStuffing.time_compile_problem.json | 1 + ...ng.ParamSmallMatrixStuffing.time_compile_problem.json | 1 + 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--- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json @@ -1 +1 @@ -[[8646, 1.2846319445000063], [8656, 1.297310968000005]] \ No newline at end of file +[[8645, 1.2846319445000063], [8655, 1.297310968000005]] \ No newline at end of file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json index fcb8ea418..33731712a 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json @@ -1 +1 @@ -[[8646, 0.2729419815000256], [8656, 0.2700837350000427]] \ No newline at end of file +[[8645, 0.2729419815000256], [8655, 0.2700837350000427]] \ No newline at end of file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json index 8e93876a2..7e45a0a75 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1196-316/num_cpu-4/numpy/os-Linux 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a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1499-278/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1499-278/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem.json @@ -1 +1 @@ -[[8609, 0.7326167124999756], [8625, 0.7366093475000071]] \ No newline at end of file +[[8609, 0.7326167124999756], [8624, 0.7366093475000071]] \ No newline at end of file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1499-278/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1499-278/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json index db471d4a3..547e69e15 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1499-278/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1499-278/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json @@ -1 +1 @@ -[[8609, 1.324621582000077], [8625, 1.3979024645000209]] \ No newline at end of file +[[8609, 1.324621582000077], [8624, 1.3979024645000209]] \ No newline at end of file diff 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file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1773-886/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1773-886/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json index cd2210840..70c45a9c2 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1773-886/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az1773-886/num_cpu-4/numpy/os-Linux 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a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem.json @@ -1 +1 @@ -[[8625, 0.7313522164999995], [8628, 0.7320427084999892]] \ No newline at end of file +[[8624, 0.7313522164999995], [8627, 0.7320427084999892]] \ No newline at end of file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json index 2555502d4..4d0984a27 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem.json @@ -1 +1 @@ -[[8625, 1.3547839880000083], [8628, 1.2915327339999578]] \ No newline at end of file +[[8624, 1.3547839880000083], [8627, 1.2915327339999578]] \ No newline at end of file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json index 2b1f02e17..80ddbaccf 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az2021-977/num_cpu-4/numpy/os-Linux 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b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json index f7aa34f37..e2307087f 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem.json @@ -1 +1 @@ -[[8643, 0.2771859535000658], [8646, 0.2643317305000039]] \ No newline at end of file +[[8642, 0.2771859535000658], [8645, 0.2643317305000039]] \ No newline at end of file diff --git a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json index 389008b1b..71db18351 100644 --- a/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 6.5.0-1025-azure/osqp/pybind11/python-3.9/ram-16364592/scipy/scs/matrix_stuffing.SmallMatrixStuffing.time_compile_problem.json +++ b/docs/graphs/arch-x86_64/branch-HEAD/cpu-AMD EPYC 7763 64-Core Processor/ecos/machine-fv-az575-701/num_cpu-4/numpy/os-Linux 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5.15.0-1014-azure", "ram": "7110360", "python": "3.8", "osqp": "", "ecos": "", "scs": "", "numpy": "", "scipy": "", "branch": "HEAD", "pybind11": null}], "benchmarks": {"finance.CVaRBenchmark.time_compile_problem": {"code": "class CVaRBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n # Replaced real data with random values\n np.random.seed(0)\n price_scenarios = np.random.randn(131072, 192)\n forward_price_scenarios = np.random.randn(131072, 192)\n asset_energy_limits = np.random.randn(192, 2)\n bid_curve_prices = np.random.randn(192, 3)\n cvar_prob = 0.95\n cvar_kappa = 2.0\n \n num_scenarios, num_assets = price_scenarios.shape\n num_energy_segments = bid_curve_prices.shape[1] + 1\n \n price_segments = np.sum(\n forward_price_scenarios[:, :, None] > bid_curve_prices[None], axis=2\n )\n price_segments_flat = (\n price_segments + np.arange(num_assets) * num_energy_segments\n ).reshape(-1)\n price_segments_sp = sp.coo_matrix(\n (\n np.ones(num_scenarios * num_assets),\n (np.arange(num_scenarios * num_assets), price_segments_flat),\n ),\n shape=(num_scenarios * num_assets, num_assets * num_energy_segments),\n )\n \n prices_flat = (price_scenarios - forward_price_scenarios).reshape(-1)\n scenario_sum = sp.coo_matrix(\n (\n np.ones(num_scenarios * num_assets),\n (\n np.repeat(np.arange(num_scenarios), num_assets),\n np.arange(num_scenarios * num_assets),\n ),\n )\n )\n \n A = np.asarray((scenario_sum @ sp.diags(prices_flat) @ price_segments_sp).todense())\n c = np.mean(A, axis=0)\n gamma = 1.0 / (1.0 - cvar_prob) / num_scenarios\n kappa = cvar_kappa\n x_min = np.tile(asset_energy_limits[:, 0:1], (1, num_energy_segments)).reshape(-1)\n x_max = np.tile(asset_energy_limits[:, 1:2], (1, num_energy_segments)).reshape(-1)\n \n alpha = cp.Variable()\n x = cp.Variable(num_assets * num_energy_segments)\n \n problem = cp.Problem(\n cp.Minimize(c.T @ x),\n [\n alpha + gamma * cp.sum(cp.pos(A @ x - alpha)) <= kappa,\n x >= x_min,\n x <= x_max,\n ],\n )\n \n self.problem = problem", "min_run_count": 2, "name": "finance.CVaRBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 999, "type": "time", "unit": "seconds", "version": "2a130e57c07794b9a40b3e5a9a60e571c4fd23d7bc4db3d214200c409f06decc", "warmup_time": -1}, "finance.FactorCovarianceModel.time_compile_problem": {"code": "class FactorCovarianceModel:\n def time_compile_problem(self):\n self.Lmax.value = 2\n self.gamma.value = 0.1\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = 14500\n m = 250\n np.random.seed(1)\n mu = np.abs(np.random.randn(n, 1))\n Sigma_tilde = np.random.randn(m, m)\n Sigma_tilde = Sigma_tilde.T.dot(Sigma_tilde)\n D = sp.diags(np.random.uniform(0, 0.9, size=n))\n F = np.random.randn(n, m)\n \n # Factor model portfolio optimization.\n w = cp.Variable(n)\n f = cp.Variable(m)\n self.gamma = cp.Parameter(nonneg=True)\n self.Lmax = cp.Parameter()\n ret = mu.T @ w\n risk = cp.quad_form(f, Sigma_tilde, assume_PSD=True) + cp.sum_squares(np.sqrt(D) @ w)\n objective = cp.Maximize(ret - self.gamma * risk)\n constraints = [cp.sum(w) == 1, f == F.T @ w, cp.norm(w, 1) <= self.Lmax]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "finance.FactorCovarianceModel.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "05132bd6e29e6e5d0fb509a69889005e56a9f1cce65b2369b4881a41e0a2ad6d", "warmup_time": -1}, "gini_portfolio.Cajas.time_compile_problem": {"code": "class Cajas:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n rs = np.random.RandomState(123)\n N = 50\n T = 350\n cov = rs.rand(N,N) * 1.5 - 0.5\n cov = cov @ cov.T/1000 + np.diag(rs.rand(N) * 0.7 + 0.3)/1000\n mean = np.zeros(N) + 1/1000\n returns = st.multivariate_normal.rvs(mean=mean, cov=cov, size=T, random_state=rs)\n \n w = cp.Variable((N,1))\n constraints = []\n a = cp.Variable((T,1))\n b = cp.Variable((T,1))\n y = cp.Variable((T,1))\n owa_w = []\n for i in range(1,T+1):\n owa_w.append(2*i - 1 - T)\n owa_w = np.array(owa_w) / (T * (T-1))\n constraints = [returns @ w == y,\n w >= 0,\n cp.sum(w) == 1]\n for i in range(T):\n constraints += [a[i] + b >= cp.multiply(owa_w[i], y)]\n risk = cp.sum(a + b)\n objective = cp.Minimize(risk * 1000)\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "gini_portfolio.Cajas.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f73882f5ed1e93da259896f54d6c7fa2f2684002ff47c9b4354e1a607c04eefd", "warmup_time": -1}, "gini_portfolio.Murray.time_compile_problem": {"code": "class Murray:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n rs = np.random.RandomState(123)\n N = 50\n T = 350\n cov = rs.rand(N, N) * 1.5 - 0.5\n cov = cov @ cov.T/1000 + np.diag(rs.rand(N) * 0.7 + 0.3)/1000\n mean = np.zeros(N) + 1/1000\n returns = st.multivariate_normal.rvs(mean=mean, cov=cov, size=T, random_state=rs)\n \n d = cp.Variable((int(T * (T - 1) / 2), 1))\n w = cp.Variable((N, 1))\n constraints = []\n ret_w = cp.Variable((T, 1))\n constraints.append(ret_w == returns @ w)\n mat = np.zeros((d.shape[0], T))\n \"\"\"\n We need to create a vector that has the following entries:\n ret_w[i] - ret_w[j]\n for j in range(T), for i in range(j+1, T).\n We do this by building a numpy array of mostly 0's and 1's.\n (It would be better to use SciPy sparse matrix objects.)\n \"\"\"\n ell = 0\n for j in range(T):\n for i in range(j + 1, T):\n # write to mat so that (mat @ ret_w)[ell] == var_i - var_j\n mat[ell, i] = 1\n mat[ell, j] = -1\n ell += 1\n all_pairs_ret_diff = mat @ ret_w\n constraints += [d >= all_pairs_ret_diff,\n d >= -all_pairs_ret_diff,\n w >= 0,\n cp.sum(w) == 1]\n risk = cp.sum(d) / ((T - 1) * T)\n objective = cp.Minimize(risk * 1000)\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "gini_portfolio.Murray.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "8cfb1206dbc330b898207e296b54520e413bbbf8d0e6d24dbc616a5663846716", "warmup_time": -1}, "gini_portfolio.Yitzhaki.time_compile_problem": {"code": "class Yitzhaki:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n rs = np.random.RandomState(123)\n N = 50\n T = 150\n cov = rs.rand(N, N) * 1.5 - 0.5\n cov = cov @ cov.T/1000 + np.diag(rs.rand(N) * 0.7 + 0.3)/1000\n mean = np.zeros(N) + 1/1000\n returns = st.multivariate_normal.rvs(mean=mean, cov=cov, size=T, random_state=rs)\n assets = ['Asset ' + str(i) for i in range(1, N + 1)]\n D = np.array([]).reshape(0, len(assets))\n for j in range(0, returns.shape[0]-1):\n D = np.concatenate((D, returns[j+1:] - returns[j, :]), axis=0)\n \n d = cp.Variable((int(T * (T - 1) / 2), 1))\n w = cp.Variable((N, 1))\n constraints = []\n all_pairs_ret_diff = D @ w\n constraints += [d >= all_pairs_ret_diff,\n d >= -all_pairs_ret_diff,\n w >= 0,\n cp.sum(w) == 1]\n risk = cp.sum(d) / ((T - 1) * T)\n objective = cp.Minimize(risk * 1000)\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "gini_portfolio.Yitzhaki.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7cc5581008142caaccffd2307bc2f406b6984042d224f9c61979bc06835538c1", "warmup_time": -1}, "high_dim_convex_plasticity.ConvexPlasticity.time_compile_problem": {"code": "class ConvexPlasticity:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n E_dim = 70e3 # [Pa]\n E = 70e3 / E_dim # [-] Young's modulus\n nu = 0.3 # [-] Poisson's ratio\n sig0 = 250 / E_dim # [-] yield strength\n Et = E / 100.0 # [-] tangent modulus\n H = E * Et / (E - Et) # [-] hardening modulus\n \n l, m = E * nu / (1 + nu) / (1 - 2 * nu), E / 2 / (1 + nu) # noqa: E741\n \n # Elasticity stiffness matrix\n C = np.array(\n [\n [l + 2 * m, l, l, 0],\n [l, l + 2 * m, l, 0],\n [l, l, l + 2 * m, 0],\n [0, 0, 0, 2 * m],\n ]\n )\n S = np.linalg.inv(C)\n \n def criterion(sig: cp.Variable, p: cp.Variable):\n N = p.size\n dev = np.array(\n [\n [2 / 3.0, -1 / 3.0, -1 / 3.0, 0],\n [-1 / 3.0, 2 / 3.0, -1 / 3.0, 0],\n [-1 / 3.0, -1 / 3.0, 2 / 3.0, 0],\n [0, 0, 0, 1.0],\n ]\n )\n \n sig0_vec = np.repeat(sig0, N)\n s = dev @ sig\n return [np.sqrt(3 / 2) * cp.norm(s, axis=0) <= sig0_vec + p * H]\n \n particular_deps = np.array(\n [7.24276667e-04, -3.41842340e-04, 0.00000000e00, -2.65071262e-05]\n )\n deps_dummy = np.full((4000, 4), particular_deps)\n \n N = 500\n deps_local_values = deps_dummy[:N]\n \n deps = cp.Parameter((4, N), name=\"deps\")\n sig_old = cp.Parameter((4, N), name=\"sig_old\")\n sig_elas = sig_old + C @ deps\n sig = cp.Variable((4, N), name=\"sig\")\n p_old = cp.Parameter((N,), nonneg=True, name=\"p_old\")\n p = cp.Variable((N,), nonneg=True, name=\"p\")\n \n sig_old.value = np.zeros((4, N))\n deps.value = np.zeros((4, N))\n p_old.value = np.zeros((N,))\n deps.value = deps_local_values.T\n \n delta_sig = sig - sig_elas\n D = H * np.eye(N)\n S_sparsed = block_diag([S for _ in range(N)])\n delta_sig_vector = cp.reshape(delta_sig, (N * 4))\n \n elastic_energy = cp.quad_form(delta_sig_vector, S_sparsed, assume_PSD=True)\n target_expression = 0.5 * elastic_energy + 0.5 * cp.quad_form(p - p_old, D)\n constraints = criterion(sig, p)\n \n problem = cp.Problem(cp.Minimize(target_expression), constraints)\n \n self.problem = problem", "min_run_count": 2, "name": "high_dim_convex_plasticity.ConvexPlasticity.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f324901397b434a5e2523f3afcf900cf3aaee0691640a60489228589016e2024", "warmup_time": -1}, "huber_regression.HuberRegression.time_compile_problem": {"code": "class HuberRegression:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n np.random.seed(1)\n n = 3000\n p = 0.12\n SAMPLES = int(1.5 * n)\n beta_true = 5 * np.random.normal(size=(n, 1))\n X = np.random.randn(n, SAMPLES)\n v = np.random.normal(size=(SAMPLES, 1))\n \n # Generate the sign changes.\n factor = 2 * np.random.binomial(1, 1 - p, size=(SAMPLES, 1)) - 1\n Y = factor * X.T.dot(beta_true) + v\n beta = cp.Variable((n, 1))\n # Solve a huber regression problem\n cost = cp.sum(cp.huber(X.T @ beta - Y, 1))\n objective = cp.Minimize(cost)\n problem = cp.Problem(objective)\n self.problem = problem", "min_run_count": 2, "name": "huber_regression.HuberRegression.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "370ff6507bcc626b80bd3ca3731e4b1df2171a5b7510fb7e43a09572a8b2c71e", "warmup_time": -1}, "matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem": {"code": "class ConeMatrixStuffingBench:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 2000\n n = 2000\n A = np.random.randn(m, n)\n C = np.random.rand(m // 2)\n b = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d125bf0d94750542fdd88d64036c1b7330ce9bca4303bab8471c4a1e598d38c8", "warmup_time": -1}, "matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem": {"code": "class ParamConeMatrixStuffing:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 200\n n = 200\n A = cp.Parameter((m, n))\n C = cp.Parameter(m // 2)\n b = cp.Parameter(m)\n A.value = np.random.randn(m, n)\n C.value = np.random.rand(m // 2)\n b.value = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "56696934c045873dd9388d364c2cce6d29ae04d32455761d720571f0c7b0bd61", "warmup_time": -1}, "matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem": {"code": "class ParamSmallMatrixStuffing:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 100\n n = 100\n A = cp.Parameter((m, n))\n C = cp.Parameter(m // 2)\n b = cp.Parameter(m)\n A.value = np.random.randn(m, n)\n C.value = np.random.rand(m // 2)\n b.value = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "81b8fb141469c5aabb73461185550a9956d91a1ba85995911648101740f3d591", "warmup_time": -1}, "matrix_stuffing.SmallMatrixStuffing.time_compile_problem": {"code": "class SmallMatrixStuffing:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 200\n n = 200\n A = np.random.randn(m, n)\n C = np.random.rand(m // 2)\n b = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.SmallMatrixStuffing.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "ad3de554debe53fd9b8f4d8110f4b3534a4f1a6db334b2934d28c3a47c88b849", "warmup_time": -1}, "optimal_advertising.OptimalAdvertising.time_compile_problem": {"code": "class OptimalAdvertising:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n np.random.seed(1)\n m = 250\n n = 1000\n SCALE = 10000\n B = np.random.lognormal(mean=8, size=(m, 1)) + 10000\n B = 1000 * np.round(B / 1000)\n \n P_ad = np.random.uniform(size=(m, 1))\n P_time = np.random.uniform(size=(1, n))\n P = P_ad.dot(P_time)\n \n T = np.sin(np.linspace(-2 * np.pi / 2, 2 * np.pi - 2 * np.pi / 2, n)) * SCALE\n T += -np.min(T) + SCALE\n c = np.random.uniform(size=(m,))\n c *= 0.6 * T.sum() / c.sum()\n c = 1000 * np.round(c / 1000)\n R = np.array([np.random.lognormal(c.min() / c[i]) for i in range(m)])\n \n # Solve optimization problem\n D = cp.Variable((m, n))\n Si = [cp.minimum(R[i] * P[i, :] @ D[i, :].T, B[i]) for i in range(m)]\n objective = cp.Maximize(cp.sum(Si))\n constraints = [D >= 0, D.T @ np.ones(m) <= T, D @ np.ones(n) >= c]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "optimal_advertising.OptimalAdvertising.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c1a33d0f2763ff52fb848b520767be561f657e1ed9704dd7f263d252b8391fd5", "warmup_time": -1}, "quantum_hilbert_matrix.QuantumHilbertMatrix.time_compile_problem": {"code": "class QuantumHilbertMatrix:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n d = 2 # local hilbert space dimension\n N = 3 # number of qubits\n dim = d**N # hilbert space dimension of full system\n \n I = sparse.identity(dim) # sparse identity matrix # noqa: E741\n \n rng = default_rng(0)\n # sparse matrices with the same density as the ones in my problem\n rhs = sparse.random(\n m=dim**2, n=dim**2, density=0.05810546875, random_state=rng\n )\n A = sparse.random(\n m=dim**2, n=dim**2, density=0.29058837890625, random_state=rng\n )\n \n AxI = sparse.kron(\n A, I\n ) # doing this beforehand to decrease number of expressions in cvxpy cost function\n \n X = cp.Variable((dim**2, dim**2), PSD=True) # PSD variable\n lhs = cp.partial_trace(\n cp.kron(I, cp.partial_transpose(X, dims=[dim, dim], axis=0)) @ AxI,\n dims=[dim, dim, dim],\n axis=1,\n )\n cost = cp.sum_squares(lhs - rhs)\n problem = cp.Problem(cp.Minimize(cost))\n self.problem = problem", "min_run_count": 2, "name": "quantum_hilbert_matrix.QuantumHilbertMatrix.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "97bae84afd5421f74a8aea822cc392e4cee9a0fe5ee8ef1e4640ed869ec19306", "warmup_time": -1}, "sdp_segfault_1132_benchmark.SDPSegfault1132Benchmark.time_compile_problem": {"code": "class SDPSegfault1132Benchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = 100\n alpha = 1\n np.random.seed(0)\n points = np.random.rand(5, n)\n xtx = points.T @ points\n xtxd = np.diag(xtx)\n e = np.ones((n,))\n D = np.outer(e, xtxd) - 2 * xtx + np.outer(xtxd, e)\n # Construct W\n W = np.ones((n, n))\n \n # Define V and e\n n = D.shape[0]\n x = -1 / (n + np.sqrt(n))\n y = -1 / np.sqrt(n)\n V = np.ones((n, n - 1))\n V[0, :] *= y\n V[1:, :] *= x\n V[1:, :] += np.eye(n - 1)\n e = np.ones((n, 1))\n \n # Solve optimization problem\n G = cp.Variable((n - 1, n - 1), PSD=True)\n A = cp.kron(e, cp.reshape(cp.diag(V @ G @ V.T), (1, n)))\n B = cp.kron(e.T, cp.reshape(cp.diag(V @ G @ V.T), (n, 1)))\n C = alpha * cp.norm(cp.multiply(W, A + B - 2 * V @ G @ V.T - D), p=\"fro\")\n objective = cp.Maximize(cp.trace(G) - C)\n problem = cp.Problem(objective, [])\n self.problem = problem", "min_run_count": 2, "name": "sdp_segfault_1132_benchmark.SDPSegfault1132Benchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 999, "type": "time", "unit": "seconds", "version": "ed808360d3230f364014822f855b979ad49e444e8bdeb093f06f2452c0c8deb5", "warmup_time": -1}, "semidefinite_programming.SemidefiniteProgramming.time_compile_problem": {"code": "class SemidefiniteProgramming:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n # Generate a random SDP.\n n = 125\n p = 75\n np.random.seed(1)\n C = np.random.randn(n, n)\n A = []\n b = []\n for i in range(p):\n A.append(np.random.randn(n, n))\n b.append(np.random.randn())\n \n # Define and solve the CVXPY problem.\n # Create a symmetric matrix variable.\n X = cp.Variable((n, n), symmetric=True)\n constraints = [X >> 0]\n constraints += [cp.trace(A[i] @ X) == b[i] for i in range(p)]\n objective = cp.Minimize(cp.trace(C @ X))\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "semidefinite_programming.SemidefiniteProgramming.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b8f19042c318507db3a79f3dfda663b94bfe6f0af2b43bf818e5c7f75fce4c59", "warmup_time": -1}, "simple_LP_benchmarks.SimpleFullyParametrizedLPBenchmark.time_compile_problem": {"code": "class SimpleFullyParametrizedLPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = int(1e4)\n p = cp.Parameter(n)\n x = cp.Variable(n)\n objective = cp.Minimize(p @ x)\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_LP_benchmarks.SimpleFullyParametrizedLPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "107c2224d8c77ed7a8e5e3a7ae3545dc2357739b7a7f37f02d5d8372fa10db09", "warmup_time": -1}, "simple_LP_benchmarks.SimpleLPBenchmark.time_compile_problem": {"code": "class SimpleLPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = int(1e7)\n c = np.arange(n)\n x = cp.Variable(n)\n objective = cp.Minimize(c @ x)\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_LP_benchmarks.SimpleLPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b81438c7919e9cb9394ca22fad039deba1325311fe54c0ae980452b93f34fe01", "warmup_time": -1}, "simple_LP_benchmarks.SimpleScalarParametrizedLPBenchmark.time_compile_problem": {"code": "class SimpleScalarParametrizedLPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = int(1e6)\n p = cp.Parameter()\n c = np.arange(n)\n x = cp.Variable(n)\n objective = cp.Minimize((p * c) @ x)\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_LP_benchmarks.SimpleScalarParametrizedLPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "643deb6bc63211d7d322ac7ec9fe22c254ac6122afa51d53009157dbec777013", "warmup_time": -1}, "simple_QP_benchmarks.LeastSquares.time_compile_problem": {"code": "class LeastSquares:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.OSQP)\n\n def setup(self):\n m = 5000\n n = 1000\n np.random.seed(1)\n A = np.random.randn(m, n)\n b = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum_squares(A @ x - b)\n problem = cp.Problem(cp.Minimize(cost))\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.LeastSquares.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "a780c97f86faca1c0b75992ac06434a9cd0e17cbfd9b466cbd20060838729607", "warmup_time": -1}, "simple_QP_benchmarks.ParametrizedQPBenchmark.time_compile_problem": {"code": "class ParametrizedQPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.OSQP)\n\n def setup(self):\n m = 250\n n = 100\n np.random.seed(1)\n A = cp.Parameter((m, n))\n b = cp.Parameter((m,))\n \n x = cp.Variable(n)\n objective = cp.Minimize(cp.sum_squares(A @ x - b))\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.ParametrizedQPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "36472f339d84464f1e377c9506cb50ecd65a0e06ed4d9eca2ffce97b5cb11423", "warmup_time": -1}, "simple_QP_benchmarks.SimpleQPBenchmark.time_compile_problem": {"code": "class SimpleQPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.OSQP)\n\n def setup(self):\n m = 2000\n n = 400\n p = 5\n np.random.seed(1)\n P = np.random.randn(n, n)\n P = np.matmul(P.T, P)\n q = np.random.randn(n)\n G = np.random.randn(m, n)\n h = np.matmul(G, np.random.randn(n))\n A = np.random.randn(p, n)\n b = np.random.randn(p)\n \n x = cp.Variable(n)\n problem = cp.Problem(cp.Minimize((1 / 2) * cp.quad_form(x, P, assume_PSD=True) +\n cp.matmul(q.T, x)),\n [cp.matmul(G, x) <= h,\n cp.matmul(A, x) == b])\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.SimpleQPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5bb63593b1e46938dfb8ae3467c4ffdf42583fe6a97e92734161e86d0df2ee6e", "warmup_time": -1}, "simple_QP_benchmarks.UnconstrainedQP.time_compile_problem": {"code": "class UnconstrainedQP:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n N_r = 16\n N_t = 2\n N_s = 6\n \n x = np.random.randint(2, size=N_s * N_r * N_t)\n \n H = dft(N_s * N_r * N_t) * 1j\n H_H = H.conj().T\n \n err = np.random.random(N_r)\n Err = np.kron(np.diag(np.ones(N_s * N_t)), np.diag(err))\n \n y = H_H @ Err @ H @ x\n \n var = cp.Variable(shape=(N_r), complex=True)\n Err_est = cp.kron(np.diag(np.ones(N_s * N_t)), cp.diag(var))\n \n res = cp.norm2(H_H @ Err_est @ H @ x - y)\n \n problem = cp.Problem(cp.Minimize(res))\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.UnconstrainedQP.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "27046933d1d1fd5cf23189b6ce8bad5798f552613ac37bbbf517fba0d7083efb", "warmup_time": -1}, "slow_pruning_1668_benchmark.SlowPruningBenchmark.time_compile_problem": {"code": "class SlowPruningBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n \"\"\"Regression test for https://github.com/cvxpy/cvxpy/issues/1668\n \n Pruning matrices caused order-of-magnitude slow downs in compilation times.\n \"\"\"\n s = 2000\n t = 10\n x = np.linspace(-100.0, 100.0, s)\n rows = 50\n var = cp.Variable(shape=(rows, t))\n \n cost = cp.sum_squares(\n var @ np.tile(np.array([x]), t).reshape((t, x.shape[0]))\n - np.tile(x, rows).reshape((rows, s))\n )\n objective = cp.Minimize(cost)\n problem = cp.Problem(objective)\n self.problem = problem", "min_run_count": 2, "name": "slow_pruning_1668_benchmark.SlowPruningBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3844770ebf2a43f77aa8977342dd47622ffa2d45a39a6c5d93faf072035c21da", "warmup_time": -1}, "svm_l1_regularization.SVMWithL1Regularization.time_compile_problem": {"code": "class SVMWithL1Regularization:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n np.random.seed(1)\n n = 500\n m = 25000\n DENSITY = 0.2\n beta_true = np.random.randn(n, 1)\n idxs = np.random.choice(range(n), int((1 - DENSITY) * n), replace=False)\n for idx in idxs:\n beta_true[idx] = 0\n \n offset = 0\n sigma = 45\n X = np.random.normal(0, 5, size=(m, n))\n Y = np.sign(X.dot(beta_true) + offset + np.random.normal(0, sigma, size=(m, 1)))\n \n # Solve optimization problem\n beta = cp.Variable((n, 1))\n v = cp.Variable()\n loss = cp.sum(cp.pos(1 - cp.multiply(Y, X @ beta - v)))\n reg = cp.norm(beta, 1)\n lambd = cp.Parameter(nonneg=True)\n objective = cp.Minimize(loss / m + lambd * reg)\n problem = cp.Problem(objective)\n self.problem = problem", "min_run_count": 2, "name": "svm_l1_regularization.SVMWithL1Regularization.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "03d77ce97e937084e6f88faf3ac4cad3d9e26690ed9d022ea9190f1e15001ea5", "warmup_time": -1}, "tv_inpainting.TvInpainting.time_compile_problem": {"code": "class TvInpainting:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n Uorig = np.random.randn(512, 512, 3)\n rows, cols, colors = Uorig.shape\n known = np.zeros((rows, cols, colors))\n for i in range(rows):\n for j in range(cols):\n if np.random.random() > 0.7:\n for k in range(colors):\n known[i, j, k] = 1\n \n Ucorr = known * Uorig # This is elementwise mult on numpy arrays.\n variables = []\n constraints = []\n for i in range(colors):\n U = cp.Variable(shape=(rows, cols))\n variables.append(U)\n constraints.append(cp.multiply(\n known[:, :, i], U) == cp.multiply(\n known[:, :, i], Ucorr[:, :, i]))\n problem = cp.Problem(cp.Minimize(cp.tv(*variables)), constraints)\n self.problem = problem", "min_run_count": 2, "name": "tv_inpainting.TvInpainting.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9e77ab42b03f5c2ebc54cdf3184e3fa6b04773212bbfdc734635987fd52f5678", "warmup_time": -1}}, "machines": {"fv-az1501-326": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1501-326", "num_cpu": "4", "os": "Linux 6.2.0-1019-azure", "ram": "16365028", "version": 1}, "fv-az1532-831": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1532-831", "num_cpu": "4", "os": "Linux 6.5.0-1018-azure", "ram": "16364592", "version": 1}, "fv-az453-168": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz", "machine": "fv-az453-168", "num_cpu": "2", "os": "Linux 5.15.0-1017-azure", "ram": "7110652", "version": 1}, "fv-az1456-93": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1456-93", "num_cpu": "4", "os": "Linux 6.5.0-1016-azure", "ram": "16364600", "version": 1}, "fv-az410-709": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8370C CPU @ 2.80GHz", "machine": "fv-az410-709", "num_cpu": "2", "os": "Linux 5.15.0-1041-azure", "ram": "7098320", "version": 1}, "fv-az847-400": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az847-400", "num_cpu": "4", "os": "Linux 6.5.0-1025-azure", "ram": "16364592", "version": 1}, "fv-az162-235": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz", "machine": "fv-az162-235", "num_cpu": "2", "os": "Linux 5.15.0-1040-azure", "ram": "7098320", "version": 1}, "fv-az574-655": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8171M CPU @ 2.60GHz", "machine": "fv-az574-655", "num_cpu": "2", "os": "Linux 5.15.0-1034-azure", "ram": "7110624", "version": 1}, "fv-az1429-528": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1429-528", "num_cpu": "4", "os": "Linux 6.2.0-1018-azure", "ram": "16365028", "version": 1}, "fv-az245-751": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz", "machine": "fv-az245-751", "num_cpu": "2", "os": "Linux 5.15.0-1022-azure", "ram": "7110656", "version": 1}, "fv-az1433-392": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1433-392", "num_cpu": "4", "os": "Linux 6.5.0-1018-azure", "ram": "16364596", "version": 1}, "fv-az1208-169": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1208-169", "num_cpu": "4", "os": "Linux 6.5.0-1018-azure", "ram": "16364596", "version": 1}, "fv-az124-715": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8370C CPU @ 2.80GHz", "machine": "fv-az124-715", "num_cpu": "2", "os": "Linux 5.15.0-1017-azure", "ram": "7110652", "version": 1}, "fv-az1040-33": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1040-33", "num_cpu": "4", "os": "Linux 6.5.0-1018-azure", "ram": "16364596", "version": 1}, "fv-az348-84": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz", "machine": "fv-az348-84", "num_cpu": "2", "os": "Linux 5.15.0-1022-azure", "ram": "7110656", "version": 1}, "fv-az568-978": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az568-978", "num_cpu": "4", "os": "Linux 6.2.0-1015-azure", "ram": "16365036", "version": 1}, "fv-az617-209": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz", "machine": "fv-az617-209", "num_cpu": "2", "os": "Linux 6.2.0-1011-azure", "ram": "7088120", "version": 1}, "fv-az1788-87": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1788-87", "num_cpu": "4", "os": "Linux 6.5.0-1025-azure", "ram": "16364592", "version": 1}, "fv-az198-929": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8171M CPU @ 2.60GHz", "machine": "fv-az198-929", "num_cpu": "2", "os": "Linux 5.13.0-1031-azure", "ram": "7113128", "version": 1}, "fv-az206-634": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) 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"", "ecos": "", "scs": "", "numpy": "", "scipy": "", "branch": "HEAD", "pybind11": null}], "benchmarks": {"finance.CVaRBenchmark.time_compile_problem": {"code": "class CVaRBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n # Replaced real data with random values\n np.random.seed(0)\n price_scenarios = np.random.randn(131072, 192)\n forward_price_scenarios = np.random.randn(131072, 192)\n asset_energy_limits = np.random.randn(192, 2)\n bid_curve_prices = np.random.randn(192, 3)\n cvar_prob = 0.95\n cvar_kappa = 2.0\n \n num_scenarios, num_assets = price_scenarios.shape\n num_energy_segments = bid_curve_prices.shape[1] + 1\n \n price_segments = np.sum(\n forward_price_scenarios[:, :, None] > bid_curve_prices[None], axis=2\n )\n price_segments_flat = (\n price_segments + np.arange(num_assets) * num_energy_segments\n ).reshape(-1)\n price_segments_sp = sp.coo_matrix(\n (\n np.ones(num_scenarios * num_assets),\n (np.arange(num_scenarios * num_assets), price_segments_flat),\n ),\n shape=(num_scenarios * num_assets, num_assets * num_energy_segments),\n )\n \n prices_flat = (price_scenarios - forward_price_scenarios).reshape(-1)\n scenario_sum = sp.coo_matrix(\n (\n np.ones(num_scenarios * num_assets),\n (\n np.repeat(np.arange(num_scenarios), num_assets),\n np.arange(num_scenarios * num_assets),\n ),\n )\n )\n \n A = np.asarray((scenario_sum @ sp.diags(prices_flat) @ price_segments_sp).todense())\n c = np.mean(A, axis=0)\n gamma = 1.0 / (1.0 - cvar_prob) / num_scenarios\n kappa = cvar_kappa\n x_min = np.tile(asset_energy_limits[:, 0:1], (1, num_energy_segments)).reshape(-1)\n x_max = np.tile(asset_energy_limits[:, 1:2], (1, num_energy_segments)).reshape(-1)\n \n alpha = cp.Variable()\n x = cp.Variable(num_assets * num_energy_segments)\n \n problem = cp.Problem(\n cp.Minimize(c.T @ x),\n [\n alpha + gamma * cp.sum(cp.pos(A @ x - alpha)) <= kappa,\n x >= x_min,\n x <= x_max,\n ],\n )\n \n self.problem = problem", "min_run_count": 2, "name": "finance.CVaRBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 999, "type": "time", "unit": "seconds", "version": "2a130e57c07794b9a40b3e5a9a60e571c4fd23d7bc4db3d214200c409f06decc", "warmup_time": -1}, "finance.FactorCovarianceModel.time_compile_problem": {"code": "class FactorCovarianceModel:\n def time_compile_problem(self):\n self.Lmax.value = 2\n self.gamma.value = 0.1\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = 14500\n m = 250\n np.random.seed(1)\n mu = np.abs(np.random.randn(n, 1))\n Sigma_tilde = np.random.randn(m, m)\n Sigma_tilde = Sigma_tilde.T.dot(Sigma_tilde)\n D = sp.diags(np.random.uniform(0, 0.9, size=n))\n F = np.random.randn(n, m)\n \n # Factor model portfolio optimization.\n w = cp.Variable(n)\n f = cp.Variable(m)\n self.gamma = cp.Parameter(nonneg=True)\n self.Lmax = cp.Parameter()\n ret = mu.T @ w\n risk = cp.quad_form(f, Sigma_tilde, assume_PSD=True) + cp.sum_squares(np.sqrt(D) @ w)\n objective = cp.Maximize(ret - self.gamma * risk)\n constraints = [cp.sum(w) == 1, f == F.T @ w, cp.norm(w, 1) <= self.Lmax]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "finance.FactorCovarianceModel.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "05132bd6e29e6e5d0fb509a69889005e56a9f1cce65b2369b4881a41e0a2ad6d", "warmup_time": -1}, "gini_portfolio.Cajas.time_compile_problem": {"code": "class Cajas:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n rs = np.random.RandomState(123)\n N = 50\n T = 350\n cov = rs.rand(N,N) * 1.5 - 0.5\n cov = cov @ cov.T/1000 + np.diag(rs.rand(N) * 0.7 + 0.3)/1000\n mean = np.zeros(N) + 1/1000\n returns = st.multivariate_normal.rvs(mean=mean, cov=cov, size=T, random_state=rs)\n \n w = cp.Variable((N,1))\n constraints = []\n a = cp.Variable((T,1))\n b = cp.Variable((T,1))\n y = cp.Variable((T,1))\n owa_w = []\n for i in range(1,T+1):\n owa_w.append(2*i - 1 - T)\n owa_w = np.array(owa_w) / (T * (T-1))\n constraints = [returns @ w == y,\n w >= 0,\n cp.sum(w) == 1]\n for i in range(T):\n constraints += [a[i] + b >= cp.multiply(owa_w[i], y)]\n risk = cp.sum(a + b)\n objective = cp.Minimize(risk * 1000)\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "gini_portfolio.Cajas.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f73882f5ed1e93da259896f54d6c7fa2f2684002ff47c9b4354e1a607c04eefd", "warmup_time": -1}, "gini_portfolio.Murray.time_compile_problem": {"code": "class Murray:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n rs = np.random.RandomState(123)\n N = 50\n T = 350\n cov = rs.rand(N, N) * 1.5 - 0.5\n cov = cov @ cov.T/1000 + np.diag(rs.rand(N) * 0.7 + 0.3)/1000\n mean = np.zeros(N) + 1/1000\n returns = st.multivariate_normal.rvs(mean=mean, cov=cov, size=T, random_state=rs)\n \n d = cp.Variable((int(T * (T - 1) / 2), 1))\n w = cp.Variable((N, 1))\n constraints = []\n ret_w = cp.Variable((T, 1))\n constraints.append(ret_w == returns @ w)\n mat = np.zeros((d.shape[0], T))\n \"\"\"\n We need to create a vector that has the following entries:\n ret_w[i] - ret_w[j]\n for j in range(T), for i in range(j+1, T).\n We do this by building a numpy array of mostly 0's and 1's.\n (It would be better to use SciPy sparse matrix objects.)\n \"\"\"\n ell = 0\n for j in range(T):\n for i in range(j + 1, T):\n # write to mat so that (mat @ ret_w)[ell] == var_i - var_j\n mat[ell, i] = 1\n mat[ell, j] = -1\n ell += 1\n all_pairs_ret_diff = mat @ ret_w\n constraints += [d >= all_pairs_ret_diff,\n d >= -all_pairs_ret_diff,\n w >= 0,\n cp.sum(w) == 1]\n risk = cp.sum(d) / ((T - 1) * T)\n objective = cp.Minimize(risk * 1000)\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "gini_portfolio.Murray.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "8cfb1206dbc330b898207e296b54520e413bbbf8d0e6d24dbc616a5663846716", "warmup_time": -1}, "gini_portfolio.Yitzhaki.time_compile_problem": {"code": "class Yitzhaki:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n rs = np.random.RandomState(123)\n N = 50\n T = 150\n cov = rs.rand(N, N) * 1.5 - 0.5\n cov = cov @ cov.T/1000 + np.diag(rs.rand(N) * 0.7 + 0.3)/1000\n mean = np.zeros(N) + 1/1000\n returns = st.multivariate_normal.rvs(mean=mean, cov=cov, size=T, random_state=rs)\n assets = ['Asset ' + str(i) for i in range(1, N + 1)]\n D = np.array([]).reshape(0, len(assets))\n for j in range(0, returns.shape[0]-1):\n D = np.concatenate((D, returns[j+1:] - returns[j, :]), axis=0)\n \n d = cp.Variable((int(T * (T - 1) / 2), 1))\n w = cp.Variable((N, 1))\n constraints = []\n all_pairs_ret_diff = D @ w\n constraints += [d >= all_pairs_ret_diff,\n d >= -all_pairs_ret_diff,\n w >= 0,\n cp.sum(w) == 1]\n risk = cp.sum(d) / ((T - 1) * T)\n objective = cp.Minimize(risk * 1000)\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "gini_portfolio.Yitzhaki.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "7cc5581008142caaccffd2307bc2f406b6984042d224f9c61979bc06835538c1", "warmup_time": -1}, "high_dim_convex_plasticity.ConvexPlasticity.time_compile_problem": {"code": "class ConvexPlasticity:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n E_dim = 70e3 # [Pa]\n E = 70e3 / E_dim # [-] Young's modulus\n nu = 0.3 # [-] Poisson's ratio\n sig0 = 250 / E_dim # [-] yield strength\n Et = E / 100.0 # [-] tangent modulus\n H = E * Et / (E - Et) # [-] hardening modulus\n \n l, m = E * nu / (1 + nu) / (1 - 2 * nu), E / 2 / (1 + nu) # noqa: E741\n \n # Elasticity stiffness matrix\n C = np.array(\n [\n [l + 2 * m, l, l, 0],\n [l, l + 2 * m, l, 0],\n [l, l, l + 2 * m, 0],\n [0, 0, 0, 2 * m],\n ]\n )\n S = np.linalg.inv(C)\n \n def criterion(sig: cp.Variable, p: cp.Variable):\n N = p.size\n dev = np.array(\n [\n [2 / 3.0, -1 / 3.0, -1 / 3.0, 0],\n [-1 / 3.0, 2 / 3.0, -1 / 3.0, 0],\n [-1 / 3.0, -1 / 3.0, 2 / 3.0, 0],\n [0, 0, 0, 1.0],\n ]\n )\n \n sig0_vec = np.repeat(sig0, N)\n s = dev @ sig\n return [np.sqrt(3 / 2) * cp.norm(s, axis=0) <= sig0_vec + p * H]\n \n particular_deps = np.array(\n [7.24276667e-04, -3.41842340e-04, 0.00000000e00, -2.65071262e-05]\n )\n deps_dummy = np.full((4000, 4), particular_deps)\n \n N = 500\n deps_local_values = deps_dummy[:N]\n \n deps = cp.Parameter((4, N), name=\"deps\")\n sig_old = cp.Parameter((4, N), name=\"sig_old\")\n sig_elas = sig_old + C @ deps\n sig = cp.Variable((4, N), name=\"sig\")\n p_old = cp.Parameter((N,), nonneg=True, name=\"p_old\")\n p = cp.Variable((N,), nonneg=True, name=\"p\")\n \n sig_old.value = np.zeros((4, N))\n deps.value = np.zeros((4, N))\n p_old.value = np.zeros((N,))\n deps.value = deps_local_values.T\n \n delta_sig = sig - sig_elas\n D = H * np.eye(N)\n S_sparsed = block_diag([S for _ in range(N)])\n delta_sig_vector = cp.reshape(delta_sig, (N * 4))\n \n elastic_energy = cp.quad_form(delta_sig_vector, S_sparsed, assume_PSD=True)\n target_expression = 0.5 * elastic_energy + 0.5 * cp.quad_form(p - p_old, D)\n constraints = criterion(sig, p)\n \n problem = cp.Problem(cp.Minimize(target_expression), constraints)\n \n self.problem = problem", "min_run_count": 2, "name": "high_dim_convex_plasticity.ConvexPlasticity.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "f324901397b434a5e2523f3afcf900cf3aaee0691640a60489228589016e2024", "warmup_time": -1}, "huber_regression.HuberRegression.time_compile_problem": {"code": "class HuberRegression:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n np.random.seed(1)\n n = 3000\n p = 0.12\n SAMPLES = int(1.5 * n)\n beta_true = 5 * np.random.normal(size=(n, 1))\n X = np.random.randn(n, SAMPLES)\n v = np.random.normal(size=(SAMPLES, 1))\n \n # Generate the sign changes.\n factor = 2 * np.random.binomial(1, 1 - p, size=(SAMPLES, 1)) - 1\n Y = factor * X.T.dot(beta_true) + v\n beta = cp.Variable((n, 1))\n # Solve a huber regression problem\n cost = cp.sum(cp.huber(X.T @ beta - Y, 1))\n objective = cp.Minimize(cost)\n problem = cp.Problem(objective)\n self.problem = problem", "min_run_count": 2, "name": "huber_regression.HuberRegression.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "370ff6507bcc626b80bd3ca3731e4b1df2171a5b7510fb7e43a09572a8b2c71e", "warmup_time": -1}, "matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem": {"code": "class ConeMatrixStuffingBench:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 2000\n n = 2000\n A = np.random.randn(m, n)\n C = np.random.rand(m // 2)\n b = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.ConeMatrixStuffingBench.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "d125bf0d94750542fdd88d64036c1b7330ce9bca4303bab8471c4a1e598d38c8", "warmup_time": -1}, "matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem": {"code": "class ParamConeMatrixStuffing:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 200\n n = 200\n A = cp.Parameter((m, n))\n C = cp.Parameter(m // 2)\n b = cp.Parameter(m)\n A.value = np.random.randn(m, n)\n C.value = np.random.rand(m // 2)\n b.value = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "56696934c045873dd9388d364c2cce6d29ae04d32455761d720571f0c7b0bd61", "warmup_time": -1}, "matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem": {"code": "class ParamSmallMatrixStuffing:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 100\n n = 100\n A = cp.Parameter((m, n))\n C = cp.Parameter(m // 2)\n b = cp.Parameter(m)\n A.value = np.random.randn(m, n)\n C.value = np.random.rand(m // 2)\n b.value = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "81b8fb141469c5aabb73461185550a9956d91a1ba85995911648101740f3d591", "warmup_time": -1}, "matrix_stuffing.SmallMatrixStuffing.time_compile_problem": {"code": "class SmallMatrixStuffing:\n def time_compile_problem(self):\n cms = ConeMatrixStuffing()\n cms.apply(self.problem)\n\n def setup(self):\n m = 200\n n = 200\n A = np.random.randn(m, n)\n C = np.random.rand(m // 2)\n b = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum(A @ x)\n \n constraints = [C[i] * x[i] <= b[i] for i in range(m // 2)]\n constraints.extend([C[i] * x[m // 2 + i] == b[m // 2 + i] for i in range(m // 2)])\n \n problem = cp.Problem(cp.Minimize(cost), constraints)\n self.problem = problem", "min_run_count": 2, "name": "matrix_stuffing.SmallMatrixStuffing.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "ad3de554debe53fd9b8f4d8110f4b3534a4f1a6db334b2934d28c3a47c88b849", "warmup_time": -1}, "optimal_advertising.OptimalAdvertising.time_compile_problem": {"code": "class OptimalAdvertising:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n np.random.seed(1)\n m = 250\n n = 1000\n SCALE = 10000\n B = np.random.lognormal(mean=8, size=(m, 1)) + 10000\n B = 1000 * np.round(B / 1000)\n \n P_ad = np.random.uniform(size=(m, 1))\n P_time = np.random.uniform(size=(1, n))\n P = P_ad.dot(P_time)\n \n T = np.sin(np.linspace(-2 * np.pi / 2, 2 * np.pi - 2 * np.pi / 2, n)) * SCALE\n T += -np.min(T) + SCALE\n c = np.random.uniform(size=(m,))\n c *= 0.6 * T.sum() / c.sum()\n c = 1000 * np.round(c / 1000)\n R = np.array([np.random.lognormal(c.min() / c[i]) for i in range(m)])\n \n # Solve optimization problem\n D = cp.Variable((m, n))\n Si = [cp.minimum(R[i] * P[i, :] @ D[i, :].T, B[i]) for i in range(m)]\n objective = cp.Maximize(cp.sum(Si))\n constraints = [D >= 0, D.T @ np.ones(m) <= T, D @ np.ones(n) >= c]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "optimal_advertising.OptimalAdvertising.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "c1a33d0f2763ff52fb848b520767be561f657e1ed9704dd7f263d252b8391fd5", "warmup_time": -1}, "quantum_hilbert_matrix.QuantumHilbertMatrix.time_compile_problem": {"code": "class QuantumHilbertMatrix:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n d = 2 # local hilbert space dimension\n N = 3 # number of qubits\n dim = d**N # hilbert space dimension of full system\n \n I = sparse.identity(dim) # sparse identity matrix # noqa: E741\n \n rng = default_rng(0)\n # sparse matrices with the same density as the ones in my problem\n rhs = sparse.random(\n m=dim**2, n=dim**2, density=0.05810546875, random_state=rng\n )\n A = sparse.random(\n m=dim**2, n=dim**2, density=0.29058837890625, random_state=rng\n )\n \n AxI = sparse.kron(\n A, I\n ) # doing this beforehand to decrease number of expressions in cvxpy cost function\n \n X = cp.Variable((dim**2, dim**2), PSD=True) # PSD variable\n lhs = cp.partial_trace(\n cp.kron(I, cp.partial_transpose(X, dims=[dim, dim], axis=0)) @ AxI,\n dims=[dim, dim, dim],\n axis=1,\n )\n cost = cp.sum_squares(lhs - rhs)\n problem = cp.Problem(cp.Minimize(cost))\n self.problem = problem", "min_run_count": 2, "name": "quantum_hilbert_matrix.QuantumHilbertMatrix.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "97bae84afd5421f74a8aea822cc392e4cee9a0fe5ee8ef1e4640ed869ec19306", "warmup_time": -1}, "sdp_segfault_1132_benchmark.SDPSegfault1132Benchmark.time_compile_problem": {"code": "class SDPSegfault1132Benchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = 100\n alpha = 1\n np.random.seed(0)\n points = np.random.rand(5, n)\n xtx = points.T @ points\n xtxd = np.diag(xtx)\n e = np.ones((n,))\n D = np.outer(e, xtxd) - 2 * xtx + np.outer(xtxd, e)\n # Construct W\n W = np.ones((n, n))\n \n # Define V and e\n n = D.shape[0]\n x = -1 / (n + np.sqrt(n))\n y = -1 / np.sqrt(n)\n V = np.ones((n, n - 1))\n V[0, :] *= y\n V[1:, :] *= x\n V[1:, :] += np.eye(n - 1)\n e = np.ones((n, 1))\n \n # Solve optimization problem\n G = cp.Variable((n - 1, n - 1), PSD=True)\n A = cp.kron(e, cp.reshape(cp.diag(V @ G @ V.T), (1, n)))\n B = cp.kron(e.T, cp.reshape(cp.diag(V @ G @ V.T), (n, 1)))\n C = alpha * cp.norm(cp.multiply(W, A + B - 2 * V @ G @ V.T - D), p=\"fro\")\n objective = cp.Maximize(cp.trace(G) - C)\n problem = cp.Problem(objective, [])\n self.problem = problem", "min_run_count": 2, "name": "sdp_segfault_1132_benchmark.SDPSegfault1132Benchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 999, "type": "time", "unit": "seconds", "version": "ed808360d3230f364014822f855b979ad49e444e8bdeb093f06f2452c0c8deb5", "warmup_time": -1}, "semidefinite_programming.SemidefiniteProgramming.time_compile_problem": {"code": "class SemidefiniteProgramming:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n # Generate a random SDP.\n n = 125\n p = 75\n np.random.seed(1)\n C = np.random.randn(n, n)\n A = []\n b = []\n for i in range(p):\n A.append(np.random.randn(n, n))\n b.append(np.random.randn())\n \n # Define and solve the CVXPY problem.\n # Create a symmetric matrix variable.\n X = cp.Variable((n, n), symmetric=True)\n constraints = [X >> 0]\n constraints += [cp.trace(A[i] @ X) == b[i] for i in range(p)]\n objective = cp.Minimize(cp.trace(C @ X))\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "semidefinite_programming.SemidefiniteProgramming.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b8f19042c318507db3a79f3dfda663b94bfe6f0af2b43bf818e5c7f75fce4c59", "warmup_time": -1}, "simple_LP_benchmarks.SimpleFullyParametrizedLPBenchmark.time_compile_problem": {"code": "class SimpleFullyParametrizedLPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = int(1e4)\n p = cp.Parameter(n)\n x = cp.Variable(n)\n objective = cp.Minimize(p @ x)\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_LP_benchmarks.SimpleFullyParametrizedLPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "107c2224d8c77ed7a8e5e3a7ae3545dc2357739b7a7f37f02d5d8372fa10db09", "warmup_time": -1}, "simple_LP_benchmarks.SimpleLPBenchmark.time_compile_problem": {"code": "class SimpleLPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = int(1e7)\n c = np.arange(n)\n x = cp.Variable(n)\n objective = cp.Minimize(c @ x)\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_LP_benchmarks.SimpleLPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "b81438c7919e9cb9394ca22fad039deba1325311fe54c0ae980452b93f34fe01", "warmup_time": -1}, "simple_LP_benchmarks.SimpleScalarParametrizedLPBenchmark.time_compile_problem": {"code": "class SimpleScalarParametrizedLPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n n = int(1e6)\n p = cp.Parameter()\n c = np.arange(n)\n x = cp.Variable(n)\n objective = cp.Minimize((p * c) @ x)\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_LP_benchmarks.SimpleScalarParametrizedLPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "643deb6bc63211d7d322ac7ec9fe22c254ac6122afa51d53009157dbec777013", "warmup_time": -1}, "simple_QP_benchmarks.LeastSquares.time_compile_problem": {"code": "class LeastSquares:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.OSQP)\n\n def setup(self):\n m = 5000\n n = 1000\n np.random.seed(1)\n A = np.random.randn(m, n)\n b = np.random.randn(m)\n \n x = cp.Variable(n)\n cost = cp.sum_squares(A @ x - b)\n problem = cp.Problem(cp.Minimize(cost))\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.LeastSquares.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "a780c97f86faca1c0b75992ac06434a9cd0e17cbfd9b466cbd20060838729607", "warmup_time": -1}, "simple_QP_benchmarks.ParametrizedQPBenchmark.time_compile_problem": {"code": "class ParametrizedQPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.OSQP)\n\n def setup(self):\n m = 250\n n = 100\n np.random.seed(1)\n A = cp.Parameter((m, n))\n b = cp.Parameter((m,))\n \n x = cp.Variable(n)\n objective = cp.Minimize(cp.sum_squares(A @ x - b))\n constraints = [0 <= x, x <= 1]\n problem = cp.Problem(objective, constraints)\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.ParametrizedQPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "36472f339d84464f1e377c9506cb50ecd65a0e06ed4d9eca2ffce97b5cb11423", "warmup_time": -1}, "simple_QP_benchmarks.SimpleQPBenchmark.time_compile_problem": {"code": "class SimpleQPBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.OSQP)\n\n def setup(self):\n m = 2000\n n = 400\n p = 5\n np.random.seed(1)\n P = np.random.randn(n, n)\n P = np.matmul(P.T, P)\n q = np.random.randn(n)\n G = np.random.randn(m, n)\n h = np.matmul(G, np.random.randn(n))\n A = np.random.randn(p, n)\n b = np.random.randn(p)\n \n x = cp.Variable(n)\n problem = cp.Problem(cp.Minimize((1 / 2) * cp.quad_form(x, P, assume_PSD=True) +\n cp.matmul(q.T, x)),\n [cp.matmul(G, x) <= h,\n cp.matmul(A, x) == b])\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.SimpleQPBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "5bb63593b1e46938dfb8ae3467c4ffdf42583fe6a97e92734161e86d0df2ee6e", "warmup_time": -1}, "simple_QP_benchmarks.UnconstrainedQP.time_compile_problem": {"code": "class UnconstrainedQP:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n N_r = 16\n N_t = 2\n N_s = 6\n \n x = np.random.randint(2, size=N_s * N_r * N_t)\n \n H = dft(N_s * N_r * N_t) * 1j\n H_H = H.conj().T\n \n err = np.random.random(N_r)\n Err = np.kron(np.diag(np.ones(N_s * N_t)), np.diag(err))\n \n y = H_H @ Err @ H @ x\n \n var = cp.Variable(shape=(N_r), complex=True)\n Err_est = cp.kron(np.diag(np.ones(N_s * N_t)), cp.diag(var))\n \n res = cp.norm2(H_H @ Err_est @ H @ x - y)\n \n problem = cp.Problem(cp.Minimize(res))\n self.problem = problem", "min_run_count": 2, "name": "simple_QP_benchmarks.UnconstrainedQP.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "27046933d1d1fd5cf23189b6ce8bad5798f552613ac37bbbf517fba0d7083efb", "warmup_time": -1}, "slow_pruning_1668_benchmark.SlowPruningBenchmark.time_compile_problem": {"code": "class SlowPruningBenchmark:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n \"\"\"Regression test for https://github.com/cvxpy/cvxpy/issues/1668\n \n Pruning matrices caused order-of-magnitude slow downs in compilation times.\n \"\"\"\n s = 2000\n t = 10\n x = np.linspace(-100.0, 100.0, s)\n rows = 50\n var = cp.Variable(shape=(rows, t))\n \n cost = cp.sum_squares(\n var @ np.tile(np.array([x]), t).reshape((t, x.shape[0]))\n - np.tile(x, rows).reshape((rows, s))\n )\n objective = cp.Minimize(cost)\n problem = cp.Problem(objective)\n self.problem = problem", "min_run_count": 2, "name": "slow_pruning_1668_benchmark.SlowPruningBenchmark.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "3844770ebf2a43f77aa8977342dd47622ffa2d45a39a6c5d93faf072035c21da", "warmup_time": -1}, "svm_l1_regularization.SVMWithL1Regularization.time_compile_problem": {"code": "class SVMWithL1Regularization:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n np.random.seed(1)\n n = 500\n m = 25000\n DENSITY = 0.2\n beta_true = np.random.randn(n, 1)\n idxs = np.random.choice(range(n), int((1 - DENSITY) * n), replace=False)\n for idx in idxs:\n beta_true[idx] = 0\n \n offset = 0\n sigma = 45\n X = np.random.normal(0, 5, size=(m, n))\n Y = np.sign(X.dot(beta_true) + offset + np.random.normal(0, sigma, size=(m, 1)))\n \n # Solve optimization problem\n beta = cp.Variable((n, 1))\n v = cp.Variable()\n loss = cp.sum(cp.pos(1 - cp.multiply(Y, X @ beta - v)))\n reg = cp.norm(beta, 1)\n lambd = cp.Parameter(nonneg=True)\n objective = cp.Minimize(loss / m + lambd * reg)\n problem = cp.Problem(objective)\n self.problem = problem", "min_run_count": 2, "name": "svm_l1_regularization.SVMWithL1Regularization.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "03d77ce97e937084e6f88faf3ac4cad3d9e26690ed9d022ea9190f1e15001ea5", "warmup_time": -1}, "tv_inpainting.TvInpainting.time_compile_problem": {"code": "class TvInpainting:\n def time_compile_problem(self):\n self.problem.get_problem_data(solver=cp.SCS)\n\n def setup(self):\n Uorig = np.random.randn(512, 512, 3)\n rows, cols, colors = Uorig.shape\n known = np.zeros((rows, cols, colors))\n for i in range(rows):\n for j in range(cols):\n if np.random.random() > 0.7:\n for k in range(colors):\n known[i, j, k] = 1\n \n Ucorr = known * Uorig # This is elementwise mult on numpy arrays.\n variables = []\n constraints = []\n for i in range(colors):\n U = cp.Variable(shape=(rows, cols))\n variables.append(U)\n constraints.append(cp.multiply(\n known[:, :, i], U) == cp.multiply(\n known[:, :, i], Ucorr[:, :, i]))\n problem = cp.Problem(cp.Minimize(cp.tv(*variables)), constraints)\n self.problem = problem", "min_run_count": 2, "name": "tv_inpainting.TvInpainting.time_compile_problem", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "timeout": 60.0, "type": "time", "unit": "seconds", "version": "9e77ab42b03f5c2ebc54cdf3184e3fa6b04773212bbfdc734635987fd52f5678", "warmup_time": -1}}, "machines": {"fv-az1501-326": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1501-326", "num_cpu": "4", "os": "Linux 6.2.0-1019-azure", "ram": "16365028", "version": 1}, "fv-az1532-831": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1532-831", "num_cpu": "4", "os": "Linux 6.5.0-1018-azure", "ram": "16364592", "version": 1}, "fv-az453-168": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz", "machine": "fv-az453-168", "num_cpu": "2", "os": "Linux 5.15.0-1017-azure", "ram": "7110652", "version": 1}, "fv-az1456-93": {"arch": "x86_64", "cpu": "AMD EPYC 7763 64-Core Processor", "machine": "fv-az1456-93", "num_cpu": "4", "os": "Linux 6.5.0-1016-azure", "ram": "16364600", "version": 1}, "fv-az410-709": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) Platinum 8370C CPU @ 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href="index.html#matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem?cpu=AMD+EPYC+7763+64-Core+Processor&machine=fv-az1773-886&num_cpu=4&os=Linux+6.5.0-1025-azure&ram=16364592&python=3.9&commits=f7b8d1bdd3bd38f16afb8ba000c11e245d1b2b4d">5.67% regression</a> on 2024-11-01 19:09:44 in commit <a href="https://github.com/cvxpy/cvxpy/commit/f7b8d1bdd3bd38f16afb8ba000c11e245d1b2b4d">f7b8d1bd</a>.<br> +tag:cvxpy.asv,1970-01-01:/c71906ca32eb61bdcf724ef2eda3f4f4cb19f078477b4827d7d8b84adcfc9878Airspeed Velocitycvxpy performance regressions2024-11-05T23:04:03Ztag:cvxpy.asv,2024-11-05:/1493af914132be4fcffca532891e9779275e3134576b57774ef9bd45535d75ec5.52% quantum_hilbert_matrix.QuantumHilbertMatrix.time_compile_problem2024-11-05T23:04:03Z<a href="index.html#quantum_hilbert_matrix.QuantumHilbertMatrix.time_compile_problem?cpu=AMD+EPYC+7763+64-Core+Processor&machine=fv-az1116-198&num_cpu=4&os=Linux+6.5.0-1025-azure&ram=16364592&python=3.9&commits=bc2f0aacd9d925d597d217f161264df1a5a74df5">5.52% regression</a> on 2024-11-05 22:57:07 in commit <a href="https://github.com/cvxpy/cvxpy/commit/bc2f0aacd9d925d597d217f161264df1a5a74df5">bc2f0aac</a>.<br> + New value: 3.31s, old value: 3.13s.<br> + Latest value: 3.31s (5.52% worse than best value 3.13s).tag:cvxpy.asv,2024-11-05:/4abc9360a98e3a733b9c0e0cea1a36b12cdbdcceea0477321dcebc425261e2427.80% matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem2024-11-05T23:03:42Z<a href="index.html#matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem?cpu=AMD+EPYC+7763+64-Core+Processor&machine=fv-az1116-198&num_cpu=4&os=Linux+6.5.0-1025-azure&ram=16364592&python=3.9&commits=bc2f0aacd9d925d597d217f161264df1a5a74df5">7.80% regression</a> on 2024-11-05 22:57:07 in commit <a href="https://github.com/cvxpy/cvxpy/commit/bc2f0aacd9d925d597d217f161264df1a5a74df5">bc2f0aac</a>.<br> + New value: 1.38s, old value: 1.28s.<br> + Latest value: 1.38s (7.80% worse than best value 1.28s).tag:cvxpy.asv,2024-11-01:/0535fa73acdad914bd69206af7d45f4f425ab6d5335c4fedc0d771e1898ca3065.67% matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem2024-11-04T02:02:39Z<a href="index.html#matrix_stuffing.ParamSmallMatrixStuffing.time_compile_problem?cpu=AMD+EPYC+7763+64-Core+Processor&machine=fv-az1773-886&num_cpu=4&os=Linux+6.5.0-1025-azure&ram=16364592&python=3.9&commits=f7b8d1bdd3bd38f16afb8ba000c11e245d1b2b4d">5.67% regression</a> on 2024-11-01 19:09:44 in commit <a href="https://github.com/cvxpy/cvxpy/commit/f7b8d1bdd3bd38f16afb8ba000c11e245d1b2b4d">f7b8d1bd</a>.<br> New value: 272ms, old value: 258ms.<br> Latest value: 272ms (5.67% worse than best value 258ms).tag:cvxpy.asv,2024-11-01:/d28d25cd04f02463bb8a275ef66c5a50547e2f7c3bc7c11618e230479f2aad9413.39% matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem2024-11-04T02:02:30Z<a href="index.html#matrix_stuffing.ParamConeMatrixStuffing.time_compile_problem?cpu=AMD+EPYC+7763+64-Core+Processor&machine=fv-az1773-886&num_cpu=4&os=Linux+6.5.0-1025-azure&ram=16364592&python=3.9&commits=f7b8d1bdd3bd38f16afb8ba000c11e245d1b2b4d">13.39% regression</a> on 2024-11-01 19:09:44 in commit <a href="https://github.com/cvxpy/cvxpy/commit/f7b8d1bdd3bd38f16afb8ba000c11e245d1b2b4d">f7b8d1bd</a>.<br> New value: 1.36s, old value: 1.20s.<br> diff --git a/results/fv-az1116-198/bc2f0aac-virtualenv-py3.9-ecos-numpy-osqp-pybind11-scipy-scs.json b/results/fv-az1116-198/bc2f0aac-virtualenv-py3.9-ecos-numpy-osqp-pybind11-scipy-scs.json new file mode 100644 index 000000000..667919508 --- /dev/null +++ b/results/fv-az1116-198/bc2f0aac-virtualenv-py3.9-ecos-numpy-osqp-pybind11-scipy-scs.json @@ -0,0 +1 @@ +{"commit_hash": "bc2f0aacd9d925d597d217f161264df1a5a74df5", "env_name": 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