A Python-embedded modeling language for convex optimization problems.
A large scale non-linear optimization library
Incremental Potential Contact (IPC) is for robust and accurate time stepping of nonlinear elastodynamics. IPC guarantees intersection- and inversion-free trajectories regardless of materials, time-step sizes, velocities, or deformation severity.
A next-gen Lagrange-Newton solver for nonconvex optimization. It unifies barrier and SQP methods in a modern and generic way, and implements different globalization flavors (line search/trust region and merit function/filter method/funnel method). Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.
Efficient optimal control solvers for robotic systems.
A C++ interface to formulate and solve linear, quadratic and second order cone problems.
Fatrop is a nonlinear optimal control problem solver that aims to be fast, support a broad class of optimal control problems and achieve a high numerical robustness.
Hierarchical Optimization Time Integration (HOT) for efficient implicit timestepping of the material point method (MPM)
RobOptim Core Layer: interface and basic mathematical tools
A C++ Second Order Cone Solver based on Eigen
Quantum-inspired evolutionary algorithms for Optimization problems
Wolfram Language interface to the Gurobi numerical optimization library
The fdaPDE core library is a C++ header-only library for Partial Differential Equation discretization, computational geometry, unconstrained nonlinear optimization, linear algebra and much more.
Research library for compile time optimization
A simple C++ wrapper around the original Fortran L-BGSG-B routine
Numerical optimization library in C++.
SemiDefinite Programming Algorithm (SDPA) for Python
High performance optimization algorithm for nonconvex systems based on random tree search
oomph-lib is an object-oriented, open-source finite-element library for the simulation of multi-physics problems. It is developed and maintained by Matthias Heil and Andrew Hazel of the School of Mathematics at The University of Manchester, along with many other contributors.
A New Gradient Decent Algorithm with Secant Method Scaling and Finite Difference Derivatives. Faster, Smarter and Versatile
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