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SciML / Optimization.jl

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
https://docs.sciml.ai/Optimization/stable/
MIT License
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Support for `MOI.eval_constraint_jacobian_transpose_product` #808

Closed baggepinnen closed 1 month ago

baggepinnen commented 2 months ago

MathOptInterface includes a function eval_constraint_jacobian_transpose_product which some solvers, like MadNLP) can use to avoid materializing the constraint Jacobian. MadNLP will error when using Hessian approximation unless this method is available in the NLP evaluator.

Vaibhavdixit02 commented 2 months ago

You really want to look at this PR https://github.com/SciML/OptimizationBase.jl/pull/90 and https://github.com/SciML/Optimization.jl/pull/789 😅

Vaibhavdixit02 commented 1 month ago

This should be available now, let me know if it doesn't work as expected

baggepinnen commented 1 month ago

Amazing :)