more fixes

SciML · Sep 11, 2024 · 03c2708 · 03c2708
1 parent 951d661
commit 03c2708
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Showing 6 changed files with 23 additions and 16 deletions.
diff --git a/docs/src/index.md b/docs/src/index.md
@@ -203,20 +203,16 @@ versioninfo() # hide
 ```@raw html
 </details>
 ```
-
 ```@raw html
 <details><summary>A more complete overview of all dependencies and their versions is also provided.</summary>
 ```
-
 ```@example
 using Pkg # hide
 Pkg.status(; mode = PKGMODE_MANIFEST) # hide
 ```
-
 ```@raw html
 </details>
 ```
-
 ```@eval
 using TOML
 using Markdown

diff --git a/lib/OptimizationManopt/src/OptimizationManopt.jl b/lib/OptimizationManopt/src/OptimizationManopt.jl
@@ -13,9 +13,6 @@ internal state.
 abstract type AbstractManoptOptimizer end
 
 SciMLBase.supports_opt_cache_interface(opt::AbstractManoptOptimizer) = true
-SciMLBase.requiresgradient(opt::Union{GradientDescentOptimizer, ConjugateGradientDescentOptimizer, QuasiNewtonOptimizer, ConvexBundleOptimizer, FrankWolfeOptimizer}) = true
-SciMLBase.requireshessian(opt::Union{AdaptiveRegularizationCubicOptimizer, TrustRegionsOptimizer}) = true
-
 
 function __map_optimizer_args!(cache::OptimizationCache,
         opt::AbstractManoptOptimizer;
@@ -329,6 +326,15 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
 end
 
 ## Optimization.jl stuff
+function SciMLBase.requiresgradient(opt::Union{
+        GradientDescentOptimizer, ConjugateGradientDescentOptimizer,
+        QuasiNewtonOptimizer, ConvexBundleOptimizer, FrankWolfeOptimizer})
+    true
+end
+function SciMLBase.requireshessian(opt::Union{
+        AdaptiveRegularizationCubicOptimizer, TrustRegionsOptimizer})
+    true
+end
 
 function build_loss(f::OptimizationFunction, prob, cb)
     function (::AbstractManifold, θ)

diff --git a/lib/OptimizationOptimJL/test/runtests.jl b/lib/OptimizationOptimJL/test/runtests.jl
@@ -1,6 +1,6 @@
 using OptimizationOptimJL,
       OptimizationOptimJL.Optim, Optimization, ForwardDiff, Zygote, ReverseDiff.
-      Random, ModelingToolkit, Optimization.OptimizationBase.DifferentiationInterface
+Random, ModelingToolkit, Optimization.OptimizationBase.DifferentiationInterface
 using Test
 
 struct CallbackTester

diff --git a/lib/OptimizationPRIMA/src/OptimizationPRIMA.jl b/lib/OptimizationPRIMA/src/OptimizationPRIMA.jl
@@ -15,8 +15,7 @@ SciMLBase.supports_opt_cache_interface(::PRIMASolvers) = true
 SciMLBase.allowsconstraints(::Union{LINCOA, COBYLA}) = true
 SciMLBase.allowsbounds(opt::Union{BOBYQA, LINCOA, COBYLA}) = true
 SciMLBase.requiresconstraints(opt::COBYLA) = true
-SciMLBase.requiresgradient(opt::Union{BOBYQA, LINCOA, COBYLA}) = true
-SciMLBase.requiresconsjac(opt::Union{LINCOA, COBYLA}) = true
+SciMLBase.requiresconsjac(opt::COBYLA) = true
 SciMLBase.requiresconshess(opt::COBYLA) = true
 
 function Optimization.OptimizationCache(prob::SciMLBase.OptimizationProblem,
@@ -34,8 +33,14 @@ function Optimization.OptimizationCache(prob::SciMLBase.OptimizationProblem,
         throw("We evaluate the jacobian and hessian of the constraints once to automatically detect 
         linear and nonlinear constraints, please provide a valid AD backend for using COBYLA.")
     else
-        f = Optimization.instantiate_function(
-            prob.f, reinit_cache.u0, prob.f.adtype, reinit_cache.p, num_cons)
+        if opt isa COBYLA
+            f = Optimization.instantiate_function(
+                prob.f, reinit_cache.u0, prob.f.adtype, reinit_cache.p, num_cons,
+                cons_j = true, cons_h = true)
+        else
+            f = Optimization.instantiate_function(
+                prob.f, reinit_cache.u0, prob.f.adtype, reinit_cache.p, num_cons)
+        end
     end
 
     return Optimization.OptimizationCache(f, reinit_cache, prob.lb, prob.ub, prob.lcons,

diff --git a/src/sophia.jl b/src/sophia.jl
@@ -78,7 +78,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
     for _ in 1:maxiters
         for (i, d) in enumerate(data)
             f.grad(gₜ, θ, d)
-            x = cache.f(θ, cache.p, d...)
+            x = cache.f(θ, d)
             opt_state = Optimization.OptimizationState(; iter = i,
                 u = θ,
                 objective = first(x),

diff --git a/test/minibatch.jl b/test/minibatch.jl
@@ -59,9 +59,9 @@ optfun = OptimizationFunction(loss_adjoint,
 optprob = OptimizationProblem(optfun, pp, train_loader)
 
 sol = Optimization.solve(optprob,
-Optimization.Sophia(; η = 0.5,
-    λ = 0.0),
-maxiters = 1000)
+    Optimization.Sophia(; η = 0.5,
+        λ = 0.0),
+    maxiters = 1000)
 @test 10res1.objective < l1
 
 optfun = OptimizationFunction(loss_adjoint,