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OptimizationPolyalgorithms

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
0.3.5
26 days ago
Versions
11
Downloads
119 total
Dependent Packages
2
0.1.0
Published: over 3 years ago
Registry - Documentation -
0.3.5
Published: 26 days ago
Registry - Documentation -
0.3.4
Published: 4 months ago
Registry - Documentation -
0.3.3
Published: 5 months ago
Registry - Documentation -
0.3.1
Published: 5 months ago
Registry - Documentation -
0.3.2
Published: 5 months ago
Registry - Documentation -
0.3.0
Published: over 1 year ago
Registry - Documentation -
0.2.1
Published: over 5 years ago
Registry - Documentation - Download
0.2.0
Published: over 5 years ago
Registry - Documentation - Download
0.1.2
Published: over 5 years ago
Registry - Documentation - Download
0.1.1
Published: over 5 years ago
Registry - Documentation - Download
Links
Registry juliahub.com
Source Repository
Homepage Homepage
Docs Documentation
JSON API View JSON
CodeMeta codemeta.json
Package Details
PURL pkg:julia/OptimizationPolyalgorithms
spec
License MIT
First Release over 3 years ago
Last Synced 2 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
Rankings on juliahub.com
Overall Top 7.5%
Forks Top 2.1%
Funding
https://github.com/sponsors/SciML