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.
- Updated
May 11, 2025 - Julia
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.
A next-gen Lagrange-Newton (SQP + barrier) solver for nonlinearly constrained optimization
The project involves projective geometry, geometric transformations, modelling of cameras, feature extraction, stereo vision, recognition and deep learning, 3d-modelling, geometry of surfaces and their silhouettes, tracking, and visualisation.
Art of finding minimum. Python implementations from scratch.
Add a description, image, and links to the local-optimization topic page so that developers can more easily learn about it.
To associate your repository with the local-optimization topic, visit your repo's landing page and select "manage topics."