Merge pull request #809 from arismavridis/patch-1

Added precompilation for nonnegative least squares

Author: Vaibhavdixit02

lib/OptimizationOptimJL/Project.toml

@@ -8,6 +8,7 @@ Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 
 [compat]
 Optim = "1"

lib/OptimizationOptimJL/src/OptimizationOptimJL.jl

@@ -453,4 +453,34 @@ function SciMLBase.__solve(cache::OptimizationCache{
         stats = stats)
 end
 
+using PrecompileTools
+PrecompileTools.@compile_workload begin
+
+    function obj_f(x, p)
+        A = p[1]
+        b = p[2]
+        return sum((A * x - b) .^ 2)
+    end
+
+    function solve_nonnegative_least_squares(A, b, solver)
+
+        optf = Optimization.OptimizationFunction(obj_f, Optimization.AutoForwardDiff())
+        prob = Optimization.OptimizationProblem(optf, ones(size(A, 2)), (A, b), lb=zeros(size(A, 2)), ub=Inf * ones(size(A, 2)))
+        x = OptimizationOptimJL.solve(prob, solver, maxiters=5000, maxtime=100)
+
+        return x
+    end
+
+    solver_list = [OptimizationOptimJL.LBFGS(),
+        OptimizationOptimJL.ConjugateGradient(),
+        OptimizationOptimJL.GradientDescent(),
+        OptimizationOptimJL.BFGS()]
+
+    for solver in solver_list
+        x = solve_nonnegative_least_squares(rand(4, 4), rand(4), solver)
+        x = solve_nonnegative_least_squares(rand(35, 35), rand(35), solver)
+        x = solve_nonnegative_least_squares(rand(35, 10), rand(35), solver)
+    end
+end
+
 end