Update stepsize kwargs to use the default constructor

Author: Vaibhavdixit02

lib/OptimizationManopt/src/OptimizationManopt.jl

@@ -67,7 +67,7 @@ function call_manopt_optimizer(
         x0;
         stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
         evaluation::AbstractEvaluationType = Manopt.AllocatingEvaluation(),
-        stepsize::Stepsize = ArmijoLinesearchStepsize(M),
+        stepsize::Stepsize = default_stepsize(M, GradientDescentState),
         kwargs...)
     opts = gradient_descent(M,
         loss,
@@ -111,7 +111,7 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
         x0;
         stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
         evaluation::AbstractEvaluationType = InplaceEvaluation(),
-        stepsize::Stepsize = ArmijoLinesearch(M),
+        stepsize::Stepsize = default_stepsize(M, ConjugateGradientDescentState),
         kwargs...)
     opts = conjugate_gradient_descent(M,
         loss,
@@ -308,7 +308,7 @@ function call_manopt_optimizer(M::ManifoldsBase.AbstractManifold,
         stopping_criterion::Union{Manopt.StoppingCriterion, Manopt.StoppingCriterionSet},
         evaluation::AbstractEvaluationType = InplaceEvaluation(),
         retraction_method::AbstractRetractionMethod = default_retraction_method(M),
-        stepsize::Stepsize = DecreasingLength(; length = 2.0, shift = 2),
+        stepsize::Stepsize = default_stepsize(M, FrankWolfeState),
         kwargs...)
     opt = Frank_Wolfe_method(M,
         loss,

lib/OptimizationManopt/test/runtests.jl

@@ -33,7 +33,9 @@ end
     x0 = zeros(2)
     p = [1.0, 100.0]
 
-    stepsize = Manopt.ArmijoLinesearch()
+    stepsize = default_stepsize(
+        R2, GradientDescentState
+    )
     opt = OptimizationManopt.GradientDescentOptimizer()
 
     optprob_forwarddiff = OptimizationFunction(rosenbrock, Optimization.AutoEnzyme())
@@ -65,13 +67,12 @@ end
     x0 = zeros(2)
     p = [1.0, 100.0]
 
-    stepsize = Manopt.ArmijoLinesearch(R2)
     opt = OptimizationManopt.ConjugateGradientDescentOptimizer()
 
     optprob = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
     prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
-    sol = Optimization.solve(prob, opt, stepsize = stepsize)
+    sol = Optimization.solve(prob, opt)
     @test sol.minimum < 0.5
 end