# Course, tutorial

## Linear Programming

- Illinois Math 482: Linear Programming

## Convex Optimization

- A Survey of Optimization Methods From a Machine Learning Perspective | IEEE Journals & Magazine | IEEE Xplore
- Optimization by Boyd
- ML+CO @ AAAI-21
- GitHub - epfml/OptML_course: EPFL Course - Optimization for Machine Learning - CS-439
- CMU convex optimization, [Video List]
- CS/ECE/ISyE 524: Introduction to Optimization - Laurent Lessard
- https://web-app.usc.edu/soc/syllabus/20201/30126.pdf
- Online learning and online convex optimization
- Ju Sun | Provable Nonconvex Methods/Algorithms
- ADMM: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

## Combinatorial Optimization

- https://www.coursera.org/learn/discrete-optimization
- CS675 Fall 2019: Convex and Combinatorial Optimization
- http://co-at-work.zib.de
- UT Austin, Video
- CSE 711: Topics in Combinatorial Optimization and Linear Programming (Fall 2018 Seminar)
- CS 598CSC: Topics in Combinatorial Optimization: Home Page
- Combinatorial Optimization: Exact and Approximate Algorithms
- CS 586/IE 519: Combinatorial Optimization: Home Page

## Non-Convex

- [1712.07897] Non-convex Optimization for Machine Learning
- Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview | IEEE Journals & Magazine | IEEE Xplore

# Paper

## Survey

- Combinatorial optimization and reasoning with graph neural networks
- [2005.11081] Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
- A Review of combinatorial optimization with graph neural networks | IEEE Conference Publication | IEEE Xplore
- Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

## workshop

- Deep Learning and Combinatorial Optimization (Schedule) - IPAM
- SPOC18 "Machine Learning, Networks and Combinatorial Optimization" | Groupe POC

## Competition

## Paper

- ICLR 22: [2110.05291] Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
- ICLR 21 DC3: A LEARNING METHOD FOR OPTIMIZATION WITH HARD CONSTRAINTS
- NIPS 2020 Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
- NIPS 20 GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs
- Reinforcement Learning for Combinatorial Optimization
- NIPS 19: Exact Combinatorial Optimization with Graph Convolutional Neural Networks
- NIPS 17: Learning Combinatorial Optimization Algorithms over Graphs

Citation 351 -
Machine learning for combinatorial optimization: A methodological tour d’horizon

Citation 181 - Combinatorial optimization with graph convolutional networks and guided tree search,
*NIPS 18* - Learning combinatorial optimization on graphs: A survey with applications to networking
- Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems,
*ICTAI 19*

## SAT

- Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
*NIPS 20* - Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach,
*ICLR 19* - SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver,
*PMLR 19* - Learning local search heuristics for boolean satisfiability,
*NIPS 19* - G2SAT: Learning to Generate SAT Formulas,
*NIPS 19* - Combinatorial optimization with graph convolutional networks and guided tree search,
*NIPS 18* - Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
- Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning