# Tag Info

0

Despite late response, I believe openGA is the library that fits your requirements. It is supporting multi-objective problems with NSGA-III. It is based on C++ and supports Linux, Windows and OSX. The templated structure of openGA requires you to define the solution by yourself and manage it however, this means you have maximum customization on it and you ...

0

You can and should solve this problem without linear programming and apply the Bellman equation instead. Actually, the minmax theorem -- handled numerically via LP -- is only required to solve the problem where both players simultaneously choose an action. In contrast, your game consists of a two-step process, and the mathematical model should incorporate ...

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I would also like to point at MatlabAutoDiff, which supports sparse Jacobians. Have tried it myself: it is possible to compute large Jacobians (tried with N=1e5) in a small amount of time.

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Given code that computes a function $f(x)$, automatic differentiation tools produce a code that can compute $f(x)$ and its derivatives at the same time. Solving a differential equation is an entirely different problem and AD doesn't solve differential equations (although AD tools are sometimes useful in connection with PDE constrained optimization.) AD ...

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Julia has a whole ecosystem for generating sparsity patterns and doing sparse automatic differentiation in a way that mixes with scientific computing and machine learning (or scientific machine learning). Tools like SparseDiffTools.jl, ModelingToolkit.jl, and SparsityDetection.jl will do things like: Automatically find sparsity patterns from code Generate ...

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Yes, tens of minutes for the model to run is a lot. If you are using a gradient based minimization algorithm such as BFGS to calculate the parameters, you might consider using the adjoint method for computing the gradient very efficiently.

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50 is a lot of parameters. You could try doing a basic first order sensitivity analysis to determine whether you can drop any of these. Using Bayesian Optimization to minimize a cost function is one way of dealing with the problem you've encountered. But remember that your standard L2 norm might have counterintuitive behaviours in high dimensions (see On the ...

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