Welcome to the "Awesome Optimization Courses" repository! This is a curated list of (mostly) free and open educational resources for mathematical optimization.
This list tries to cover topics on discrete and combinatorial optimization, operations research, linear and nonlinear programming, integer programming, constraint programming, convex optimization, continuous optimization, or unconstrained optimization, you'll find valuable resources here to enhance your understanding of these subjects.
-
Stanford: Convex optimization I by Stephen Boyd: (YouTube) (Course Website) (edX)
Concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering. -
Stanford: Convex optimization II by Stephen Boyd: (YouTube) (Course Website) (edX)
Continuation of Convex Optimization I. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Course requirements include a substantial project. -
CMU: Convex Optimization (Fall 2018): (YouTube) by Ryan Tibshirani
-
Convex Optimization by Ahmad Bazzi (YouTube Playlist) + CVXPy tutorials
-
Convex Optimization - NPTEL by Joydeep Dutta
-
U Wisconsin-Madison: Integer Optimization - ISyE/Math/CS 728 by Alberto Del Pia
-
A Second Course in Algorithms (Stanford CS261, Winter 2016) by Tim Roughgarden
-
Combinatorial Optimization Course by Constantine Caramanis at UT Austin
-
CSE 550: Combinatorial Optimization and Intractability by Joshua J. Daymude from Arizona State University
-
U Warwick: MA252 Combinatorial Optimization by Jonathan Noel
-
Coursera: Discrete optimization (series) - The University of Melbourne
-
Texas A&M: ISEN 668: Integer Programming (partial) by Sergiy Butenko
-
U Illinois - Urbana Champaign: IE511 - Integer Programming by James Davis
-
Overview of Linear and Mixed Integer Programming YouTube Series by Mike Wagner
-
Penn State: Math484 Linear Programming - Summer 2020 by Wen Shen
- 2016 version: YouTube
-
U Wisconsin-Madison: Linear Optimization - ISyE/Math/CS/Stat 525 (Fall 2021) by Alberto Del Pia
-
Colorado State U: Math 510 - Linear Programming and Network Flows (Fall 2020) by Henry Adams
-
Advanced Operations Research - NPTEL by G. Srinivasan
-
Coursera: Operations Research (series) - National Taiwan University
-
Optimization Techniques/Operation Research Playlist by MathPod Channel
-
Operations Research I & II by Dedy Suryadi
-
An Introduction To Constraint Programming - Jacob Allen
-
EdX: Constraint Programming course - UCLouvain (LouvainX):
Understand the constraint programming paradigm. Design and implement a modern constraint programming library. Model using the constraint programming. Extend the solver with new global constraints. Design custom and black-box searches. Approach Scheduling and Vehicle Routing problems with constraint programming.
-
Optimization Algorithms by Constantine Caramanis at UT Austin
-
Optimization Methods for Machine Learning and Engineering (KIT Winter Term 20/21) by Julius Pfrommer
-
Dimitri Bertsekas's Videos on Dynamic Programming, Reinforcement Learning, etc.
-
Mathified YouTube Channle
-
Arizona Math Camp: Optimization
Local and global optimization. Unconstrained and constrained optimization. Solution function and value function. Implicit Function Theorem. Envelope Theorem. KKT conditions. Kuhn-Tucker Theorem. -
Optimization - NPTEL by A. Goswami & Debjani Chakraborty
-
Basic Course on Stochastic Programming from Instituto de Matemática Pura e Aplicada
-
Stochastic Programming by Anthony Papavasiliou
Convex Optimization:
- Convex Optimization by Boyd and Vandenberghe - PDF
- Convex Optimization Theory by Dimitri P. Bertsekas - PDF
OR:
- Operations Research An Introduction by Hamdy A. Taha - Pearson
- Introduction to Operations Research by Frederick Hillier and Gerald Lieberman - McGraw Hill
- Julia Programming for Operations Research by Changhyun Kwon - PDF - code
- Mathematical Programming and Operations Research: Modeling, Algorithms, and Complexity. Examples in Python and Julia. Edited by Robert Hildebrand - PDF
- A First Course in Linear Optimization by Jon Lee - PDF
- Decomposition Techniques in Mathematical Programming by Conejo , Castillo , Mínguez , and García-Bertrand - Springer
- Algorithms for Optimization by Mykel J. Kochenderfer and Tim A. Wheeler - PDF
- Model Building in Mathematical Programming - Introductory modeling book by H. Paul Williams - Wiley
CP:
- Handbook of Constraint Programming by F. Rossi, Peter van Beek and Toby Walsh - Amazon
Combinatorial Optimization:
- Combinatorial Optimization: Algorithms and Complexity by by Christos H. Papadimitriou and Kenneth Steiglitz - Amazon
- Combinatorial Optimization: Theory and Algorithms by Bernhard Korte and Jens Vygen - Springer
- A First Course in Combinatorial Optimization by Jon Lee - Amazon
Stochastic Optimization:
- Lectures on Stochastic Programming Modeling and Theory (SIAM) - by Shapiro, Dentcheva, and Ruszczynski - PDF
- Introductory Lectures on Stochastic Optimization by John C. Duchi - PDF
Check out More of Prof. Bertsekas's Books
- Google OR-Tools | examples | repository
- COIN-OR | repository
- MiniZinc - Constraint modeling language.
- GAMS - High-level modeling system.
- AMPL - Algebraic modeling language, both linear/integer and constraint programming.
- JuMP - Open source modeling language for Julia.
- Pyomo - Open source modeling library for Python.
- PuLP - Open source modeling library for Python.
- Scipy Optimize
Licensed/Commercial Solvers
- CPLEX - IBM
- Gurobi - Gurobi
- FICO Xpress - FICO
- LocalSolver - LocalSolver
- SCIP - Zuse Institute Berlin
- Knitro - Artelys
- Mosek - Mosek
- Concorde - UWaterloo
- Open Optimization:
a platform for sharing and collaborating on open educational resources for optimization related courses. - Introduction to Linear Programming with Python:
Introduction to Linear Programming with Python - cvxpy:
A Python-embedded modeling language for convex optimization problems. - MIPLearn:
Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML) - or-gym:
nvironments for OR and RL Research - ecole:
Extensible Combinatorial Optimization Learning Environments - pyconcorde:
Python wrapper around the Concorde TSP solver - BOTorch:
Bayesian optimization in PyTorch - EvoTorch:
Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE. - TorchOpt:
efficient library for differentiable optimization built upon PyTorch.
- CPAIOR - Integration of Constraint Programming, Artificial Intelligence, and Operations Research.
- CP Conference - Principles and Practice of Constraint Programming.
- INFORMS - Institute for Operations Research and the Management Sciences.
- CORS - Canadian Operational Research Society
We welcome contributions to this repository. If you have a course or resource that you'd like to add, please follow these guidelines:
- Make a pull request, adding the course to the relevant section with a brief description and a link to the resource.
- Use a consistent format for entries to maintain readability.
Thank you for your contributions to making this repository a valuable resource for optimization enthusiasts in the academic community!
- add books and lecture notes
- add github repos & code
- add solver/software list
- add short description
- better fomatting