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 15 articles , 1 standard article )

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  1. Mathieu Besancon, Alejandro Carderera, Sebastian Pokutta: FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients (2021) arXiv
  2. Riva-Palacio, Alan; Leisen, Fabrizio: Compound vectors of subordinators and their associated positive Lévy copulas (2021)
  3. Dahne, Joel; Salvy, Bruno: Computation of tight enclosures for Laplacian eigenvalues (2020)
  4. Després, Bruno; Ancellin, Matthieu: A functional equation with polynomial solutions and application to neural networks (2020)
  5. Guilherme Bodin, Raphael Saavedra, Cristiano Fernandes, Alexandre Street: ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models (2020) arXiv
  6. Miller, Keaton: Sharing the sacrifice, minimizing the pain: optimal wage reductions (2020)
  7. 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
  8. Tarek, Mohamed; Ray, Tapabrata: Adaptive continuation solid isotropic material with penalization for volume constrained compliance minimization (2020)
  9. Blåbäck, J.; Gautason, F. F.; Ruipérez, A.; Van Riet, T.: Anti-brane singularities as red herrings (2019)
  10. Borggaard, Jeff; Glatt-Holtz, Nathan; Krometis, Justin: GPU-accelerated particle methods for evaluation of sparse observations for inverse problems constrained by diffusion PDEs (2019)
  11. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  12. Raphael Saavedra, Guilherme Bodin, Mario Souto: StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework (2019) arXiv
  13. Boaz Blankrot; Clemens Heitzinger: ParticleScattering: Solving and optimizing multiple-scattering problems in Julia (2018) not zbMATH
  14. P. K. Mogensen; A. N. Riseth: Optim: A mathematical optimization package for Julia (2018) not zbMATH
  15. Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson: ensmallen: a flexible C++ library for efficient function optimization (2018) arXiv