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.
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Updated
May 22, 2024 - 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 solver for optimization with nonconvex objective and constraints. Reimplements filterSQP (SQP) and IPOPT (barrier/interior-point method) in a modern and modular way, and unlocks methods never seen before. Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.
Art of finding minimum. Python implementations from scratch.
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.
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