Search Results for "linear optimization"

Mathematics is a comprehensive collection of notes, resources, and references spanning a wide range of mathematical topics. The repository organizes material across pure and applied mathematics, including calculus, linear algebra, probability, statistics, and optimization. It also extends into computational and algorithmic applications of mathematics, making it a useful reference for both academic study and practical problem-solving. The goal is to provide learners, researchers, and developers with a consolidated source of foundational and advanced mathematical concepts. ...

rePhase project has moved to https://rephase.org, please go there to download rephase and to get info, support and updates. rePhase is a FIR generation tool for building fully linear-phase active crossovers with arbitrary slopes. It also let you manually compensate for the phase shifts of your loudspeakers and existing crossovers, be it active or passive. Once generated, the FIR can be applied using any hardware (openDRC, miniSHARC, Najda, ...) or software (foobar, convolver, JRiver,...

libfgen is a library that implements an efficient and customizable genetic algorithm (GA). It also provides particle swarm optimization (PSO) functionality and an interface for real-valued function minimization or model fitting. It is written in C, but can also be compiled with a C++ compiler. Both Linux and Windows are supported.

BEMAP (BEnchMarks for Automatic Parallelizer) is a benchmark used to measure performance for an automatic parallelizer. All OpenCL code benchmarks covered in this project are done step-by-step along with hand-tunning. Each tuning step executional time are measured in details with a comprehensive user interface and help option. The exact implementation in native code (C++) is also provided in each project folder for reference. By using these benchmarks, one may analyze: 1. How to...

Yet another library of convex optimization routines; this one works with the GNU scientific library. Focuses on interior point methods for linear programming, second order cone programing and semidefinite programming

A Python environment for large and sparse linear and nonlinear optimization using intuitive interfaces to linear algebra tools and subproblem solvers written in low-level languages. It provides building blocks to experiment with new ideas and algorithms.