{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Solves unconstrained and linearly constrained nonlinear minimization problems without computing derivatives, using direct search methods that work well when objective function evaluations are expensive or noisy.","skillfed_tags":["optimization","derivative-free","direct-search"],"use_cases":["Optimize expensive simulations or physical experiments where gradient computation is infeasible or prohibitively costly.","Tune hyperparameters or model coefficients when the objective is noisy or only available through sampling.","Solve bound-constrained or linearly constrained problems without implementing custom constraint handling.","Benchmark derivative-free methods on small to moderate-dimensional problems (roughly under 50 variables).","Replace gradient-based optimizers when the objective is non-smooth or discontinuous in practice."],"what_it_does":"directsearch is a Python package implementing direct search methods for derivative-free optimization. It solves minimization problems where you have an objective function but cannot or do not want to compute its gradient\u2014useful when function evaluations are expensive, noisy, or unavailable analytically. The package supports both unconstrained problems and problems with linear inequality constraints (including bounds). It depends on numpy and scipy for numerical operations.\n\nThe core interface is a single `solve()` function that takes your objective, a starting point, and optional constraint matrices, returning the approximate minimizer, its value, the number of function evaluations used, and a termination flag. The package also exposes specialized solvers: deterministic direct search, probabilistic descent variants, subspace methods for higher dimensions, and the stochastic three-points method. Configuration options include maximum evaluations, verbosity, and print frequency.","worth_installing":"Yes, if you need derivative-free optimization for expensive or noisy objectives and accept the GPL-3.0-or-later copyleft license. The package is actively maintained, has no known vulnerabilities, and installs with low friction. It is well-suited for unconstrained problems and problems with linear constraints up to moderate dimension; for very high-dimensional problems, verify that the subspace method meets your scalability needs."},"id":"directsearch","links":{"html":"https://skillfed.io/packages/directsearch","md":"https://skillfed.io/packages/directsearch.md","pypi":"https://pypi.org/project/directsearch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-04","license_spdx":null,"license_treatment":"copyleft","name":"directsearch","python_support":"supports_current","summary":"A derivative-free solver for unconstrained minimization"},"popularity":{"monthly_downloads":437576,"position":6665,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1"}