fix: identation on docstrings

.github/workflows/TagBot.yml

@@ -28,6 +28,4 @@ jobs:
       - uses: JuliaRegistries/TagBot@v1
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
-          # Edit the following line to reflect the actual name of the GitHub Secret containing your private key
           ssh: ${{ secrets.DOCUMENTER_KEY }}
-          # ssh: ${{ secrets.NAME_OF_MY_SSH_PRIVATE_KEY_SECRET }}

docs/make.jl

@@ -18,4 +18,3 @@ if !("local" in ARGS)
         repo = "github.com/SECQUOIA/TenSolver.jl.git"
     )
 end
-

src/solution.jl

@@ -15,7 +15,7 @@ sample(psi::Distribution) = ITensorMPS.sample!(psi.tensor) .- 1
 
 
 """
-  in(xs, psi::Distribution [; cutoff)
+    in(xs, psi::Distribution [; cutoff)
 
 Whether the vector `xs` has a positive probability of being sampleable from `psi`.
 When setting `cutoff`, it will be used as the minimum probability considered positive.

src/solver.jl

@@ -113,8 +113,6 @@ by finding the ground state (least eigenspace) of the Hamiltonian
 
 where D_i acts locally on the i-th qubit as [0 0; 0 1], i.e, the projection on |1>.
 
-See also [`maximize`](@ref).
-
 The optional keyword `device` controls whether the solver should run on CPU or GPU.
 For using a GPU, you can import the respective package, e.g. CUDA.jl,
 and pass its accelerator as argument.
@@ -126,6 +124,8 @@ minimize(Q; device = CUDA.cu)
 import Metal
 minimize(Q; device = Metal.mtl)
 ```
+
+See also [`maximize`](@ref).
 """
 function minimize( Q :: AbstractMatrix{T}
               , l :: Union{AbstractVector{T}, Nothing} = nothing
@@ -160,7 +160,7 @@ function minimize( Q :: AbstractMatrix{T}
 end
 
 """
-minimize(Q::Matrix, c::Number; kwargs...)
+    minimize(Q::Matrix, c::Number; kwargs...)
 
 Solve the Quadratic Unconstrained Binary Optimization problem
 without a linear term.
@@ -173,7 +173,7 @@ See also [`maximize`](@ref).
 minimize(Q :: AbstractMatrix{T}, c :: T; kwargs...) where T = minimize(Q, nothing, c; kwargs...)
 
 """
-maximize(Q::Matrix[, l::Vector[, c::Number; kwargs...)
+    maximize(Q::Matrix[, l::Vector[, c::Number; kwargs...)
 
 Solve the Quadratic Unconstrained Binary Optimization problem
 for maximization.

test/utils.jl

@@ -4,7 +4,7 @@
 =#
 
 """
-  brute_force(f, n)
+    brute_force(f, n)
 
 A QUBO solver using a brute force approach instead of Tensor networks.
 Despite being painfully slow, this is useful as a sanity check.