Expose SAMIN options (#1136)

* Update samin.jl

* Fix time and add tests

* Update Project.toml

Author: pkofod

src/multivariate/solvers/constrained/samin.jl

@@ -22,10 +22,11 @@
 ```julia
 SAMIN(; nt::Int = 5     # reduce temperature every nt*ns*dim(x_init) evaluations
         ns::Int = 5     # adjust bounds every ns*dim(x_init) evaluations
+        t0::T = 2.0     # Initial temperature
         rt::T = 0.9     # geometric temperature reduction factor: when temp changes, new temp is t=rt*t
+        r_expand::T = 10.0 # geometric temperature promotion factor for situation with low coverage: when temp changes, new temp is t=r_expand*t
+        bounds_ratio::T = 0.6 # cut-off for bounds increment (1-bounds_ratio for decrease)
         neps::Int = 5   # number of previous best values the final result is compared to
-        f_tol::T = 1e-12 # the required tolerance level for function value comparisons
-        x_tol::T = 1e-6 # the required tolerance level for x
         coverage_ok::Bool = false, # if false, increase temperature until initial parameter space is covered
         verbosity::Int = 1) # scalar: 0, 1, 2 or 3 (default = 1).
 ```
@@ -41,12 +42,13 @@ algorithm
 @with_kw struct SAMIN{T}<:AbstractConstrainedOptimizer
     nt::Int = 5 # reduce temperature every nt*ns*dim(x_init) evaluations
     ns::Int = 5 # adjust bounds every ns*dim(x_init) evaluations
+    t0::T = 2.0 # Initial temperature
     rt::T = 0.9 # geometric temperature reduction factor: when temp changes, new temp is t=rt*t
+    r_expand::T = 10.0 # geometric temperature promotion factor for situation with low coverage: when temp changes, new temp is t=r_expand*t
+    bounds_ratio::T = 0.6 # cut-off for bounds increment (1-bounds_ratio for decrease)
     neps::Int = 5 # number of previous best values the final result is compared to
-    f_tol::T = 1e-12 # the required tolerance level for function value comparisons
-    x_tol::T = 1e-6 # the required tolerance level for x
     coverage_ok::Bool = false # if false, increase temperature until initial parameter space is covered
-    verbosity::Int = 1 # scalar: 0, 1, 2 or 3 (default = 1: see final results).
+    verbosity::Int = 0 # scalar: 0, 1, 2 or 3 (default = 1: see final results).
 end
 # * verbosity: scalar: 0, 1, 2 or 3 (default = 1).
 #     * 0 = no screen output
@@ -58,22 +60,24 @@ Base.summary(::SAMIN) = "SAMIN"
 
 function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray{Tx}, method::SAMIN, options::Options = Options()) where Tx
 
-    t0 = time() # Initial time stamp used to control early stopping by options.time_limit
+    time0 = time() # Initial time stamp used to control early stopping by options.time_limit
 
     hline = "="^80
     d = NonDifferentiable(obj_fn, x)
 
     tr = OptimizationTrace{typeof(value(d)), typeof(method)}()
     tracing = options.store_trace || options.show_trace || options.extended_trace || options.callback !== nothing
 
-    @unpack nt, ns, rt, neps, f_tol, x_tol, coverage_ok, verbosity = method
+    @unpack nt, ns, t0, rt, r_expand, bounds_ratio, neps, coverage_ok, verbosity = method
     verbose = verbosity > 0
 
+    x_tol, f_tol = options.f_abstol, options.x_abstol
+
     x0 = copy(x)
     n = size(x,1) # dimension of parameter
     #  Set initial values
     nacc = 0 # total accepted trials
-    t = 2.0 # temperature - will initially rise or fall to cover parameter space. Then it will fall
+    t = t0 # temperature - will initially rise or fall to cover parameter space. Then it will fall
     converge = 0 # convergence indicator 0 (failure), 1 (normal success), or 2 (convergence but near bounds)
     x_converged = false
     f_converged = false
@@ -96,7 +100,7 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
     options.show_trace && print_header(method)
     iteration = 0
     _time = time()
-    trace!(tr, d, (x=xopt, iteration=iteration), iteration, method, options, _time-t0)
+    trace!(tr, d, (x=xopt, iteration=iteration), iteration, method, options, _time-time0)
     stopped_by_callback = false
     # main loop, first increase temperature until parameter space covered, then reduce until convergence
     while converge==0
@@ -160,12 +164,12 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
 
                     if tracing
                         # update trace; callbacks can stop routine early by returning true
-                        stopped_by_callback =  trace!(tr, d, (x=xopt,iteration=iteration), iteration, method, options, time()-t0)
+                        stopped_by_callback =  trace!(tr, d, (x=xopt,iteration=iteration), iteration, method, options, time()-time0)
                     end
 
                     # If options.iterations exceeded, terminate the algorithm
                     _time = time()
-                    if f_calls(d) >= options.iterations || _time-t0 > options.time_limit || stopped_by_callback
+                    if f_calls(d) >= options.iterations || _time-time0 > options.time_limit || stopped_by_callback
 
                         if verbose
                             println(hline)
@@ -206,7 +210,7 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
                                                                 h_calls(d),
                                                                 true,
                                                                 options.time_limit,
-                                                                _time-t0,NamedTuple())
+                                                                _time-time0,NamedTuple())
                     end
                 end
             end
@@ -215,8 +219,8 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
             for i = 1:n
                 if (lb[i] != ub[i])
                     ratio = nacp[i] / ns
-                    if(ratio > 0.6) bounds[i] = bounds[i] * (1.0 + 2.0 * (ratio - 0.6) / 0.4) end
-                    if(ratio < .4) bounds[i] = bounds[i] / (1.0 + 2.0 * ((0.4 - ratio) / 0.4)) end
+                    if(ratio > bounds_ratio) bounds[i] = bounds[i] * (1.0 + 2.0 * (ratio - 0.6) / 0.4) end
+                    if(ratio < 1-bounds_ratio) bounds[i] = bounds[i] / (1.0 + 2.0 * ((0.4 - ratio) / 0.4)) end
                     # keep within initial bounds
                     if(bounds[i] > (ub[i] - lb[i]))
                         bounds[i] = ub[i] - lb[i]
@@ -309,7 +313,7 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
             f_old = copy(fopt)
             x = copy(xopt)
         else  # coverage not ok - increase temperature quickly to expand search area
-            t *= 10.0
+            t *= r_expand
             for i = neps:-1:2
                 fstar[i] = fstar[i-1]
             end
@@ -343,7 +347,7 @@ function optimize(obj_fn, lb::AbstractArray, ub::AbstractArray, x::AbstractArray
                                             h_calls(d),
                                             true,
                                             options.time_limit,
-                                            _time-t0,
+                                            _time-time0,
                                             NamedTuple())
 
 end

test/multivariate/solvers/constrained/samin.jl

@@ -1,9 +1,20 @@
 @testset "SAMIN" begin
-    prob = MVP.UnconstrainedProblems.examples["Himmelblau"]
-
-    xtrue = prob.solutions
-    f = OptimTestProblems.MultivariateProblems.objective(prob)
-    x0 = prob.initial_x
-    res = optimize(f, x0.-100., x0.+100.0, x0, Optim.SAMIN(), Optim.Options(iterations=1000000))
-    @test Optim.minimum(res) < 1e-6
+    @testset "SAMIN $i" for i in keys(MVP.UnconstrainedProblems.examples)
+        prob = MVP.UnconstrainedProblems.examples[i]
+        if i !== "Himmelblau"
+            continue
+        end
+        xtrue = prob.solutions
+        f = OptimTestProblems.MultivariateProblems.objective(prob)
+        x0 = prob.initial_x
+        res = optimize(
+            f,
+            x0 ./ 1.1 .- 2.0,
+            x0 .* 1.1 .+ 2.0,
+            x0,
+            Optim.SAMIN(t0 = 2.0, r_expand = 9.0, verbosity = 0),
+            Optim.Options(iterations = 100000),
+        )
+        @test Optim.minimum(res) < 1e-5
+    end
 end