A Python-embedded modeling language for convex optimization problems.
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Updated
May 29, 2024 - C++
A Python-embedded modeling language for convex optimization problems.
AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.
Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
OptaPlanner quick starts for AI optimization: many use cases shown in many different technologies.
Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
Formulate trained predictors in Gurobi models
Neuromorphic mathematical optimization with Lava
A curated list of mathematical optimization courses, lectures, books, notes, libraries, frameworks and software.
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.
Deep Learning Specialization course offered by DeepLearning.AI on Coursera
A next-gen solver for optimization with nonconvex objective and constraints. Reimplements filterSQP (SQP) and IPOPT (barrier/interior-point method) in a modern and generic way, and unlocks methods never seen before. Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.
Fortran bindings for the NLopt library
This repo contains my work & The code base for this Deep Learning Specialization offered by deeplearning.AI
Investment Funnel 📈 is an open-source python platform designed for an easy development and backtesting of outperforming investment strategies.
Implementation of different techniques to solve the Set Covering Problem (SCP).
Pure Python solver for the multi-way partition problem
Distances to sets for MathOptInterface
STELA algorithm for sparsity regularized linear regression (LASSO)
Optimize both discrete and continuous variables using just a continuous optimizer such as in scipy.optimize
Libraries for reading/writing MIP problem files, invoking external MIP solvers, etc. in Haskell
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