Optim

Optim: A mathematical optimization package for Julia. Optim provides a range of optimization capabilities written in the Julia programming language (Bezanson et al. 2017). Our aim is to enable researchers, users, and other Julia packages to solve optimization problems without writing such algorithms themselves. The package supports optimization on manifolds, functions of complex numbers, and input types such as arbitrary precision vectors and matrices. We have implemented routines for derivative free, first-order, and second-order optimization methods. The user can provide derivatives themselves, or request that they are calculated using automatic differentiation or finite difference methods. The main focus of the package has currently been on unconstrained optimization, however, box-constrained optimization is supported,and a more comprehensive support for constraints is underway.


References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder: GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language (2022) not zbMATH
  2. Tangi Migot; Dominique Orban; Abel Soares Siqueira: DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility (2022) not zbMATH
  3. Mathieu Besancon, Alejandro Carderera, Sebastian Pokutta: FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients (2021) arXiv
  4. Riva-Palacio, Alan; Leisen, Fabrizio: Compound vectors of subordinators and their associated positive Lévy copulas (2021)
  5. Dahne, Joel; Salvy, Bruno: Computation of tight enclosures for Laplacian eigenvalues (2020)
  6. Després, Bruno; Ancellin, Matthieu: A functional equation with polynomial solutions and application to neural networks (2020)
  7. Guilherme Bodin, Raphael Saavedra, Cristiano Fernandes, Alexandre Street: ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models (2020) arXiv
  8. Miller, Keaton: Sharing the sacrifice, minimizing the pain: optimal wage reductions (2020)
  9. Mitchener, W. Garrett: Ranking with Hamiltonian dynamics (2020)
  10. Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari: BAT.jl - A Julia-based tool for Bayesian inference (2020) arXiv
  11. Tarek, Mohamed; Ray, Tapabrata: Adaptive continuation solid isotropic material with penalization for volume constrained compliance minimization (2020)
  12. Blåbäck, J.; Gautason, F. F.; Ruipérez, A.; Van Riet, T.: Anti-brane singularities as red herrings (2019)
  13. Borggaard, Jeff; Glatt-Holtz, Nathan; Krometis, Justin: GPU-accelerated particle methods for evaluation of sparse observations for inverse problems constrained by diffusion PDEs (2019)
  14. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  15. Raphael Saavedra, Guilherme Bodin, Mario Souto: StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework (2019) arXiv
  16. Boaz Blankrot; Clemens Heitzinger: ParticleScattering: Solving and optimizing multiple-scattering problems in Julia (2018) not zbMATH
  17. P. K. Mogensen; A. N. Riseth: Optim: A mathematical optimization package for Julia (2018) not zbMATH
  18. Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv