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OptimizationPyCMA

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Ecosystem
juliahub.com
Latest Release
1.3.0
26 days ago
Versions
5
Downloads
9 total
1.3.0
Published: 26 days ago
Registry - Documentation -
1.2.0
Published: 5 months ago
Registry - Documentation -
1.1.0
Published: 5 months ago
Registry - Documentation -
1.0.0
Published: 9 months ago
Registry - Documentation -
0.1.0
Published: 9 months ago
Registry - Documentation -
Links
Registry juliahub.com
Source Repository
Homepage Homepage
Docs Documentation
JSON API View JSON
CodeMeta codemeta.json
Package Details
PURL pkg:julia/OptimizationPyCMA
spec
License MIT
First Release 9 months ago
Last Synced 5 days ago
Keywords
optimization local-optimization global-optimization automatic-differentiation algorithmic-differentiation sciml scientific-machine-learning hacktoberfest julia mixed-integer-programming nonlinear-optimization convex-optimization derivative-free-optimization
Repository
Stars 803 on GitHub
Forks 100 on GitHub
Commits 1583
Committers 71
Avg per Author 22.296
DDS 0.53
commits.ecosyste.ms
repos.ecosyste.ms
Funding
https://github.com/sponsors/SciML