algorithms/decompsition methods/branch and bound.ipynb

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   "source": [
    "# Branch And Bound\n",
    "\n",
    "![Introduction picture](../../assets/images/bb_0.jpg \"Board Infinity picture\")"
   ]
  },
  {
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   "source": [
    "## Understanding The Algorithm\n",
    "\n",
    "The Branch and Bound algorithm is a widely recognized algorithm design paradigm used primarily for solving combinatorial optimization problems. These problems often feature exponential time complexity and may necessitate exploring all possible permutations in the worst-case scenarios. Branch and Bound techniques offer a relatively quicker solution to these complex problems.\n",
    "\n",
    "This algorithm operates on two fundamental principles. Firstly, it recursively splits the search space into smaller spaces—a process known as branching. In isolation, branching would simply be a brute-force enumeration of all possible solutions, testing each one. Secondly, the algorithm aims to minimize a function (often denoted as $f(x)$) within these smaller spaces. The combination of these two principles allows the algorithm to efficiently navigate the search space.\n",
    "\n",
    "Branch and Bound algorithms are particularly effective in finding optimal solutions for a range of optimization problems, including combinatory, discrete, and general mathematical issues. They are even capable of providing optimal solutions to NP-Hard problems by thoroughly exploring the entire search space.\n",
    "\n",
    "The algorithm is similar to backtracking in that it uses a state space tree. However, while backtracking is generally used for decision-making problems, Branch and Bound is employed for optimization and minimization problems. This distinction is crucial in understanding the application of the algorithm in various contexts.\n",
    "\n",
    "The efficiency of the Branch and Bound algorithm largely depends on the bounding function's effectiveness and the strategy used for branching and pruning. A well-designed bounding function can significantly reduce the search space, making the algorithm more efficient, especially for complex and large-scale optimization problems.\n",
    "\n",
    "The logic of the Branch and Bound algorithm can be broken down into several key steps. This algorithm is used for solving both maximization and minimization, by systematically considering candidate solutions within a defined search space. Here's an outline of its logical flow:\n",
    "\n",
    "1. **Initialization**: The process begins by initializing a bounding function, which is used to estimate the upper or lower bound of the solution within a given subset of the search space. This function is crucial in guiding the search process.\n",
    "\n",
    "2. **Branching**: The search space is recursively divided into smaller subspaces or subproblems. This process is referred to as \"branching\". Each branch represents a portion of the search space and contains a subset of the possible solutions.\n",
    "\n",
    "3. **Bounding**: For each subspace created by branching, the bounding function is applied to estimate the best possible solution within that subspace. This step is crucial as it helps in eliminating subspaces that do not lead to an optimal solution, thereby reducing the search space.\n",
    "\n",
    "4. **Pruning**: Subspaces or branches that are bound and found not to contain a feasible or optimal solution are \"pruned\" or eliminated from further consideration. This is an essential step to reduce computation time by avoiding unnecessary exploration of unpromising subspaces.\n",
    "\n",
    "5. **Search and Exploration**: The algorithm explores the remaining subspaces, continuously branching and bounding. This process is typically carried out using a priority queue or similar data structure to manage the subspaces that need to be explored based on the bounds calculated.\n",
    "\n",
    "6. **Solution Update**: Whenever a better solution is found within a subspace, it updates the current best solution. This updated solution then serves as a new benchmark for further bounding and pruning.\n",
    "\n",
    "7. **Termination**: The process continues until there are no more subspaces left to explore or the bounding function indicates that no better solution can be found. The best solution found during this process is then returned as the optimal solution."
   ]
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   "source": [
    "## Usage Examples\n",
    "\n",
    "1. **Integer Programming**: In problems where variables are required to be integers, the Branch and Bound algorithm is used to find the optimal solution. Integer programming is a critical tool in various fields like operations research, economics, and management science, where discrete decisions need to be made.\n",
    "\n",
    "2. **Nonlinear Programming**: This involves optimizing a nonlinear objective function with nonlinear constraints. Branch and Bound is employed here to efficiently navigate through the solution space to find the best possible solution.\n",
    "\n",
    "3. **Travelling Salesman Problem (TSP)**: This classic problem, which involves finding the shortest possible route that visits a series of locations and returns to the origin, makes use of Branch and Bound to determine the most efficient path.\n",
    "\n",
    "4. **Quadratic Assignment Problem (QAP)**: In this problem, Branch and Bound is used to assign a set of facilities to a set of locations in the most optimal way, considering the distance and flow between the facilities.\n",
    "\n",
    "5. **Maximum Satisfiability Problem (MAX-SAT)**: This is a problem in computer science and mathematical logic where Branch and Bound is used to determine the arrangement of variables that maximizes the number of satisfied clauses in a Boolean formula.\n",
    "\n",
    "6. **Job Assignment Problem**: This is a real-world scenario where jobs must be assigned to workers in a way that minimizes the total cost. The algorithm navigates through different combinations of job assignments to find the most cost-effective distribution."
   ]
  },
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   "source": [
    "## Strengths\n",
    "\n",
    "1. **Reduced Time Complexity**: One of the primary advantages of the Branch and Bound algorithm is that it does not require the exploration of all nodes in a tree. This selective exploration significantly reduces the time complexity compared to other algorithms that might require a more exhaustive search.\n",
    "\n",
    "2. **Optimal Solution**: When the problem size is manageable, and branching can be completed in a reasonable time, the Branch and Bound algorithm efficiently finds an optimal solution. This is particularly valuable in combinatorial optimization problems where finding the best solution is crucial.\n",
    "\n",
    "3. **Pruning Efficiency**: The Branch and Bound algorithm includes a pruning technique that allows for the evaluation and elimination of certain paths based on their comparison with others. This process helps in disregarding paths that are not promising, thereby focusing the search on more viable solutions. This method is especially efficient in ruling out paths that are not worth exploring.\n",
    "\n",
    "4. **Low Memory Usage and Best Partial Path Availability**: The algorithm is known for its low memory usage, which is a significant advantage when dealing with large datasets or complex problems. Additionally, it always maintains information about the best partial path explored so far, which can be useful in intermediate stages of problem-solving.\n",
    "\n",
    "5. **Completeness and Optimality**: Branch and Bound is both complete and optimal, meaning it will search all possible solutions and find the best one. This ensures that no potential solution is overlooked, and the best possible outcome is achieved."
   ]
  },
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   "source": [
    "## Weaknesses\n",
    "\n",
    "1. **Time-Consuming**: The algorithm can be quite time-consuming, particularly as the size of the problem increases. In the worst case, the number of nodes in the tree can become very large, leading to a significant increase in computation time.\n",
    "\n",
    "2. **Difficulty in Parallelization**: Parallelizing the Branch and Bound algorithm is extremely challenging. This limitation can be a significant drawback in environments where parallel processing could be beneficial.\n",
    "\n",
    "3. **Dependency on Efficient Estimation of Bounds**: The algorithm's effectiveness heavily relies on the efficient estimation of lower and upper bounds of regions or branches in the search space. If no bounds are available or if they are not accurately estimated, the algorithm can degenerate into performing an exhaustive search.\n",
    "\n",
    "4. **Potential for Exponential Time Complexity**: In the worst-case scenario, the Branch and Bound algorithm can lead to exponential time complexities. This happens particularly when the algorithm has to explore a vast number of branches in the search tree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ede478d",
   "metadata": {},
   "source": [
    "## Python Demonstration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac5411d",
   "metadata": {},
   "source": [
    "### Problem initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c418c639-2bfa-462a-ad4c-f3c426a8ac0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Metropolis', (67, 83)),\n",
       " ('Emerald City', (17, 92)),\n",
       " ('Neverwinter', (39, 59)),\n",
       " ('Riverwood', (69, 57)),\n",
       " ('Vault 111', (98, 61)),\n",
       " ('Pleasantville', (88, 4)),\n",
       " ('Kakariko Village', (22, 81)),\n",
       " ('Ironforge', (72, 19))]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from assets.datasets.TSP import TravellingSalesman\n",
    "\n",
    "tsp = TravellingSalesman(8, seed=None)\n",
    "\n",
    "tsp.cities # jupyter print if nothing after\n",
    "\n",
    "### ! Optionnal ! \n",
    "### Choose the starting city using next line\n",
    "# tsp.set_starting_point('Sigil')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving with base algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum cost: 276.68970956974897\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "from queue import PriorityQueue\n",
    "\n",
    "class Node:\n",
    "    \"\"\"\n",
    "    Represents a node in a Branch and Bound decision tree.\n",
    "\n",
    "    Attributes:\n",
    "    - path (list): The path traversed until the current node.\n",
    "    - reduced_matrix (list of lists): The reduced cost matrix at the current node.\n",
    "    - cost (int): The total cost incurred in the path until the current node.\n",
    "    - vertex (int): The current vertex in the decision tree.\n",
    "    - level (int): The level of the node in the decision tree.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, path, reduced_matrix, cost, vertex, level):\n",
    "        self.path = path\n",
    "        self.reduced_matrix = reduced_matrix\n",
    "        self.cost = cost\n",
    "        self.vertex = vertex\n",
    "        self.level = level\n",
    "\n",
    "    def __lt__(self, other):\n",
    "        \"\"\"\n",
    "        Define a comparison operation (less than) for PriorityQueue, based on the cost.\n",
    "\n",
    "        Args:\n",
    "        - other (Node): Another node to compare with.\n",
    "\n",
    "        Returns:\n",
    "        - bool: True if the cost of this node is less than the other node.\n",
    "        \"\"\"\n",
    "        return self.cost < other.cost\n",
    "\n",
    "def reduce_matrix(matrix, n):\n",
    "    \"\"\"\n",
    "    Reduce a cost matrix and calculate the reduction cost.\n",
    "\n",
    "    Args:\n",
    "    - matrix (list of lists): The cost matrix to be reduced.\n",
    "    - n (int): The dimension of the matrix.\n",
    "\n",
    "    Returns:\n",
    "    - int: The total reduction cost.\n",
    "    \"\"\"\n",
    "    # Reduce each row and calculate row reduction cost\n",
    "    row_reduce = [min(row) if min(row) != sys.maxsize else 0 for row in matrix]\n",
    "    \n",
    "    # Reduce each column and calculate column reduction cost\n",
    "    col_reduce = [min(matrix[i][j] for i in range(n)) if min(matrix[i][j] for i in range(n)) != sys.maxsize else 0 for j in range(n)]\n",
    "\n",
    "    # Reduce the matrix elements\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            if matrix[i][j] != sys.maxsize and row_reduce[i] != sys.maxsize and col_reduce[j] != sys.maxsize:\n",
    "                matrix[i][j] -= (row_reduce[i] + col_reduce[j])\n",
    "\n",
    "    # Calculate total reduction cost\n",
    "    cost = sum(row_reduce) + sum(col_reduce)\n",
    "    return cost\n",
    "\n",
    "def copy_matrix(matrix, n):\n",
    "    \"\"\"\n",
    "    Create a deep copy of a matrix.\n",
    "\n",
    "    Args:\n",
    "    - matrix (list of lists): The matrix to be copied.\n",
    "    - n (int): The dimension of the matrix.\n",
    "\n",
    "    Returns:\n",
    "    - list of lists: A deep copy of the provided matrix.\n",
    "    \"\"\"\n",
    "    new_matrix = [row[:] for row in matrix]\n",
    "    return new_matrix\n",
    "\n",
    "\n",
    "\n",
    "def tsp_branch_and_bound(tsp):\n",
    "    \"\"\"\n",
    "    Solve the Traveling Salesman Problem (TSP) using the Branch and Bound algorithm.\n",
    "\n",
    "    Args:\n",
    "    - tsp (TSP instance): An instance of the TSP problem, with cities and methods to get distances.\n",
    "\n",
    "    Returns:\n",
    "    - tuple: A tuple containing the best path found and its cost.\n",
    "    \"\"\"\n",
    "    # Number of cities in the TSP instance.\n",
    "    n = len(tsp.cities)\n",
    "\n",
    "    # Create a distance matrix.\n",
    "    distances = [\n",
    "        [0 if i == j else tsp.get_distance(tsp.cities[i][0], tsp.cities[j][0]) for j in range(n)]\n",
    "        for i in range(n)\n",
    "    ]\n",
    "\n",
    "    # Priority Queue to store live nodes of the search tree.\n",
    "    pq = PriorityQueue()\n",
    "\n",
    "    # Initial reduced matrix and its cost.\n",
    "    initial_matrix = copy_matrix(distances, n)\n",
    "    initial_cost = reduce_matrix(initial_matrix, n)\n",
    "\n",
    "    # Create the first node (starting city) and add it to the queue.\n",
    "    initial_node = Node(path=[0], reduced_matrix=initial_matrix, cost=initial_cost, vertex=0, level=1)\n",
    "    pq.put(initial_node)\n",
    "\n",
    "    # Initialize minimum cost to infinity.\n",
    "    min_cost = sys.maxsize\n",
    "    best_path = []\n",
    "\n",
    "    # Explore nodes using Branch and Bound.\n",
    "    while not pq.empty():\n",
    "        current_node = pq.get()\n",
    "\n",
    "        # Check if all cities are visited.\n",
    "        if current_node.level == n:\n",
    "            # Form a path back to the starting city and check the cost.\n",
    "            current_path = current_node.path + [0]\n",
    "            current_cost = current_node.cost\n",
    "            if current_cost < min_cost:\n",
    "                min_cost = current_cost\n",
    "                best_path = current_path\n",
    "            continue\n",
    "\n",
    "        # Iterate through all vertices to find feasible vertices.\n",
    "        for i in range(n):\n",
    "            if distances[current_node.vertex][i] != sys.maxsize and i not in current_node.path:\n",
    "                new_matrix = copy_matrix(current_node.reduced_matrix, n)\n",
    "\n",
    "                # Set outgoing edges and the incoming edge of the selected vertex as infinity.\n",
    "                for j in range(n):\n",
    "                    new_matrix[current_node.vertex][j] = sys.maxsize\n",
    "                    new_matrix[j][i] = sys.maxsize\n",
    "                new_matrix[i][0] = sys.maxsize\n",
    "\n",
    "                # Calculate the cost of the child node.\n",
    "                cost = current_node.cost + current_node.reduced_matrix[current_node.vertex][i] + reduce_matrix(new_matrix, n)\n",
    "                new_node = Node(path=current_node.path + [i], reduced_matrix=new_matrix, cost=cost, vertex=i, level=current_node.level + 1)\n",
    "                pq.put(new_node)\n",
    "\n",
    "    best_path.reverse()\n",
    "    # Return the minimum cost and the best path found.\n",
    "    return best_path, min_cost\n",
    "\n",
    "\n",
    "# Usage\n",
    "path, cost = tsp_branch_and_bound(tsp)\n",
    "print(\"Minimum cost:\", cost)\n",
    "tsp.draw_path(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    " This branch and bound implementation is slow. The bellow PulP implementation shows much better performances.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving using PulP library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tour:  [(0, 1), (1, 6), (2, 3), (3, 7), (4, 0), (5, 4), (6, 2), (7, 5)]\n",
      "Minimum cost: 276.6897095697489\n",
      "Path: [0, 1, 6, 2, 3, 7, 5, 4, 0]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pulp\n",
    "from itertools import product\n",
    "\n",
    "def solve_tsp_with_pulp(tsp):\n",
    "    \"\"\"\n",
    "    Solve the Traveling Salesman Problem using linear programming with PuLP.\n",
    "\n",
    "    Args:\n",
    "    - tsp (TSP instance): An instance of the TSP problem, with cities and methods to get distances.\n",
    "\n",
    "    Returns:\n",
    "    - tuple: A tuple containing the tour as a list of (i, j) pairs and the total cost.\n",
    "    \"\"\"\n",
    "    n = len(tsp.cities)\n",
    "    # Create a distance matrix.\n",
    "    distances = [\n",
    "        [0 if i == j else tsp.get_distance(tsp.cities[i][0], tsp.cities[j][0]) for j in range(n)]\n",
    "        for i in range(n)\n",
    "    ]\n",
    "\n",
    "    # Create the problem variable to contain the problem data.\n",
    "    prob = pulp.LpProblem(\"TSP\", pulp.LpMinimize)\n",
    "\n",
    "    # A matrix of binary variables. x[i][j] is 1 if the path goes from city i to j.\n",
    "    x = pulp.LpVariable.dicts(\"x\", (range(n), range(n)), 0, 1, pulp.LpBinary)\n",
    "\n",
    "    # Objective function - minimize the total distance.\n",
    "    prob += pulp.lpSum(distances[i][j] * x[i][j] for i in range(n) for j in range(n))\n",
    "\n",
    "    # Constraints: Exactly one outgoing and one incoming edge for each city.\n",
    "    for i in range(n):\n",
    "        prob += pulp.lpSum(x[i][j] for j in range(n) if i != j) == 1\n",
    "        prob += pulp.lpSum(x[j][i] for j in range(n) if i != j) == 1\n",
    "\n",
    "    # Subtour elimination constraints.\n",
    "    u = pulp.LpVariable.dicts(\"u\", range(n), 0, n-1, pulp.LpContinuous)\n",
    "    for i, j in product(range(1, n), repeat=2):\n",
    "        if i != j:\n",
    "            prob += u[i] - u[j] + n * x[i][j] <= n-1\n",
    "\n",
    "    # Solve the problem.\n",
    "    prob.solve(pulp.apis.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "    # Extract the solution.\n",
    "    tour = [(i, j) for i in range(n) for j in range(n) if pulp.value(x[i][j]) == 1]\n",
    "\n",
    "    # Return the ordered tour and the total cost.\n",
    "    return tour, pulp.value(prob.objective)\n",
    "\n",
    "def reconstruct_path(tour, n):\n",
    "    \"\"\"\n",
    "    Reconstruct the path from the tour.\n",
    "\n",
    "    Args:\n",
    "    - tour (list of tuples): The tour as a list of (i, j) pairs.\n",
    "    - n (int): The number of cities.\n",
    "\n",
    "    Returns:\n",
    "    - list: The ordered path of the tour.\n",
    "    \"\"\"\n",
    "    path = [0] # Start from the first city.\n",
    "    while len(path) < n:\n",
    "        for i, j in tour:\n",
    "            if i == path[-1]:\n",
    "                path.append(j)\n",
    "                break\n",
    "    path.append(0) # Return to the starting city.\n",
    "    return path\n",
    "\n",
    "# Usage\n",
    "tour, cost = solve_tsp_with_pulp(tsp)\n",
    "ordered_path = reconstruct_path(tour, len(tsp.cities))\n",
    "\n",
    "print(\"Minimum cost:\", cost)\n",
    "print(\"Path:\", ordered_path)\n",
    "\n",
    "# Draw the path using TSP's draw_path method\n",
    "tsp.draw_path(ordered_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddb9c9f",
   "metadata": {},
   "source": [
    "### Knapsack problem with PulP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099b1032",
   "metadata": {},
   "source": [
    "#### Problem initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "615b3dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total weight (taking everything): 7.650000000000001 kg \n",
      "Total value (taking everything): 1562 $\n"
     ]
    }
   ],
   "source": [
    "from assets.datasets.knapsack import Knapsack\n",
    "\n",
    "# Initialize Knapsack\n",
    "knapsack = Knapsack(20, seed=42) # Gets random x items\n",
    "items = knapsack.items\n",
    "knapsack.show_state()\n",
    "\n",
    "knapsack.weight_limit = 3.5\n",
    "# knapsack.weight_limit:\n",
    "# Specifies the maximum weight capacity of the knapsack. It is a constraint for the problem, determining which\n",
    "# combinations of items can be considered valid solutions. Solutions that exceed this weight are penalized or\n",
    "# deemed infeasible, ensuring the knapsack is not overloaded."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd738d4",
   "metadata": {},
   "source": [
    "#### Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d46f560b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pulp import LpProblem, LpVariable, LpInteger, LpMaximize, value, PULP_CBC_CMD\n",
    "\n",
    "# Create a PuLP problem to model the Knapsack Problem\n",
    "problem = LpProblem(\"Knapsack_Problem\", LpMaximize)\n",
    "\n",
    "# Create a dictionary of PuLP variables with item names as keys\n",
    "# These variables are binary, indicating whether an item is included (1) or not (0).\n",
    "x = LpVariable.dicts(\"item\", [item['name'] for item in items], cat=LpInteger, lowBound=0, upBound=1)\n",
    "\n",
    "# Objective Function: Maximize the total value of items included in the knapsack\n",
    "problem += sum(item['value'] * x[item['name']] for item in items)\n",
    "\n",
    "# Constraint: Total weight of items included should not exceed the knapsack capacity\n",
    "problem += sum(item['weight'] * x[item['name']] for item in items) <= knapsack.weight_limit\n",
    "\n",
    "# Solve the problem using PuLP's built-in solver\n",
    "problem.solve(PULP_CBC_CMD(msg=False))  # msg=False disables solver logs\n",
    "\n",
    "# Extract the solution: a list of items to include in the knapsack\n",
    "knapsack.solution = [item for item in items if x[item['name']].varValue == 1]\n",
    "\n",
    "# Plot the solution using a custom function (assuming this function is implemented)\n",
    "# This function might visualize the cumulative value of items in the knapsack.\n",
    "knapsack.plot_cumulative_knapsack_with_value()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d457aaf3-dad6-4935-8c0a-e0e59091d3cc",
   "metadata": {},
   "source": [
    "End of demonstration\n",
    "\n",
    "---\n",
    "## Practical Optimization Tools\n",
    "\n",
    "1. [**PuLP (Python)**](https://coin-or.github.io/pulp/): PuLP is a Python library for linear programming. It allows users to formulate their optimization problems in a very readable and intuitive way and can be used with different solvers, including those implementing the branch and bound method. PuLP is distinguished by its ease of use and its integration into the Python ecosystem, making it an ideal choice for projects requiring linear and integer optimization in Python.\n",
    "\n",
    "2. [**SCIP (C)**](https://scipopt.org): SCIP is a problem-solving library for combinatorial optimization, which includes branch and bound algorithms. It is written in C and is particularly renowned for its flexibility and efficiency in solving integer optimization problems.\n",
    "\n",
    "3. [**COIN-OR (C++)**](https://www.coin-or.org): The Computational Infrastructure for Operations Research (COIN-OR) is an initiative to develop open-source software for the analysis and solving of problems in operations research. Several of its projects, written in C++, implement the Branch and Bound algorithm.\n",
    "\n",
    "4. [**MATLAB Optimization Toolbox (MATLAB)**](https://www.mathworks.com/products/matlab.html): MATLAB's Optimization Toolbox offers functions to solve linear, quadratic, nonlinear, and mixed-integer optimization problems. Although MATLAB is not open-source, it is widely used in academic and industrial environments.\n",
    "\n",
    "5. [**GLPK (GNU Linear Programming Kit) (C)**](https://www.gnu.org/software/glpk/): GLPK provides a solver for linear programming (LP) and integer linear programming (ILP) problems. It is written in C and is a popular open-source option for linear and integer optimization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sources\n",
    "\n",
    "| Sources |\n",
    "|---------|\n",
    "| [Branch and Bound Algorithm - Geeksforgeeks](https://www.geeksforgeeks.org/branch-and-bound-algorithm/) |\n",
    "| [Branch and bound - Wikipedia](https://en.wikipedia.org/wiki/Branch_and_bound) |\n",
    "| [Branch and Bound Algorithm - Coding ninjas](https://www.codingninjas.com/studio/library/branch-and-bound-algorithm) |\n",
    "| [Branch and Bound - Wallstreetmojo](https://www.wallstreetmojo.com/branch-and-bound/) |\n",
    "| [Introduction to Branch and Bound – Data Structures and Algorithms Tutorial - Geeksforgeeks](https://www.geeksforgeeks.org/introduction-to-branch-and-bound-data-structures-and-algorithms-tutorial/) |\n",
    "| [Branch and Bound Algorithm - Baeldung](https://www.baeldung.com/cs/branch-and-bound) |\n",
    "| [Branch and Bound Technique - Opengenus](https://iq.opengenus.org/branch-and-bound/) |\n",
    "| [ALGORITHMS BRANCH AND BOUND - Stanford](https://cs.stanford.edu/people/eroberts/courses/soco/projects/2003-04/intelligent-search/branch.html) |\n",
    "| [Branch and Bound - seas.gwu.edu](https://www2.seas.gwu.edu/~ayoussef/cs6212/branchandbound.html) |\n",
    "| [Intro picture - Boardinfinity](https://www.boardinfinity.com/blog/branch-and-bound-algorithm/) |\n"
   ]
  }
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