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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) does not support box constraints. Either remove the `lb` or `ub` bounds passed to `OptimizationProblem` or use a different algorithm."))
throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires box constraints. Either pass `lb` and `ub` bounds to `OptimizationProblem` or use a different algorithm."))
throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) does not support constraints. Either remove the `cons` function passed to `OptimizationFunction` or use a different algorithm."))
throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) does not support callbacks, remove the `callback` keyword argument from the `solve` call."))
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SciMLBase.requiresgradient(alg) &&
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!(prob.f isa SciMLBase.AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires gradients, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoForwardDiff())` or pass it in with `grad` kwarg."))
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SciMLBase.requireshessian(alg) &&
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!(prob.f isa SciMLBase.AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires hessians, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoFiniteDiff(); kwargs...)` or pass them in with `hess` kwarg."))
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SciMLBase.requiresconsjac(alg) &&
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!(prob.f isa SciMLBase.AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires constraint jacobians, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoFiniteDiff(); kwargs...)` or pass them in with `cons` kwarg."))
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SciMLBase.requiresconshess(alg) &&
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!(prob.f isa SciMLBase.AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires constraint hessians, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoFiniteDiff(), AutoFiniteDiff(hess=true); kwargs...)` or pass them in with `cons` kwarg."))
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return
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end
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# Base solver dispatch functions (these will be extended by specific solver packages)
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