AI4Math-Computational-Mathematics

Route AI4Math computational mathematics tasks to the appropriate bundled skill package.

Use when reproducing, deploying, diagnosing, tuning, visualizing, or reporting computational math research code with a coding agent

This skill should be used when the user asks to search for external algorithms, find related implementations, discover alternative approaches, or look up papers and code for computational math optimization methods.

This skill should be used when the user asks to tune parameters, run hyperparameter search, optimize algorithm settings, or find the best configuration for a computational math method.

Use when classifying a computational math repository or task into a broad domain before choosing specialist Skills, references, runtimes, and validation evidence.

Use when a user starts an end-to-end computational math research-code reproduction, deployment, tuning, visualization, or reporting workflow with a coding agent.

This skill should be used when the user asks to detect what algorithm family a piece of code uses, identify optimization methods, or classify computational math approaches such as ADMM, PPA, proximal gradient, or primal-dual methods.

This skill should be used when the user asks to set up an environment, check dependencies, prepare deployment, or analyze how to run code in a specific computational environment.

This skill should be used when an execution fails, an error occurs, a command times out, or the user asks to diagnose what went wrong with a computational math experiment.

This skill should be used when the user wants to set up checkpoints, record approval decisions, gate execution pending human confirmation, or manage human-in-the-loop review points in a computational math workflow.

Use when MATLAB, Octave, MATLAB MCP, toolboxes, licenses, or coding-agent configuration must be checked or configured before MATLAB code can run.

Use when a computational math repository or task uses MATLAB files, MATLAB README commands, MATLAB toolboxes, or MATLAB MCP execution tools.

This skill should be used when the user asks to analyze, reproduce, or run a computational math repository. Use when inspecting source code, detecting algorithms, planning runs, executing experiments, or collecting results from optimization code.

This skill should be used when the user asks to generate a report, summarize results, write up findings, or create documentation for a computational math reproduction or tuning experiment.

This skill should be used when the user asks to plot results, visualize convergence, generate charts, or create figures for optimization algorithms and tuning experiments.

Use when a coding agent needs to explain, derive, or run finite element analysis workflows, including mesh discretization, element and basis selection, PDE strong-to-weak form conversion, matrix assembly, boundary-condition handling, sparse solves, and result review for teaching or small reproducible examples.

Use when a coding agent must compute, route, validate, or explain topological, geometric, or algebraic invariants such as homology, cohomology, Betti numbers, torsion, Euler characteristic, persistent homology, knot or manifold invariants, group homology, Hilbert series, Hilbert polynomials, Betti tables, dimensions, degrees, primary decompositions, or related invariants from mathematical objects, code, data, or papers.

Fits linear, polynomial, and nonlinear models to data using least squares regression. Supports ordinary least squares (OLS), weighted least squares (WLS), Ridge/Lasso regularization, custom nonlinear curve fitting, constrained least squares, MCMC Bayesian fitting, and statistical inference via numpy, scipy, scikit-learn, statsmodels, lmfit, cvxpy, iminuit, nlopt, jaxopt, and emcee. When no local library is available, searches GitHub for open-source fitting code. Uses for regression, trend analysis, curve fitting, and parameter estimation.