Combinatorial Optimization
291 papers with code • 0 benchmarks • 2 datasets
Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
Benchmarks
These leaderboards are used to track progress in Combinatorial Optimization
Libraries
Use these libraries to find Combinatorial Optimization models and implementations
MemeTracker
CVRPTW
Most implemented papers
Exploratory Combinatorial Optimization with Reinforcement Learning
tomdbar/eco-dqn
•
•
Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph.
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
qiang-ma/graph-pointer-network
•
•
Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an optimal city permutation under constraints.
Learn to Design the Heuristics for Vehicle Routing Problem
This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP).
Rethinking Differentiable Search for Mixed-Precision Neural Networks
zhaoweicai/EdMIPS
•
•
CVPR 2020
Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices.
Exploring the Loss Landscape in Neural Architecture Search
In this work, we show that (1) the simplest hill-climbing algorithm is a powerful baseline for NAS, and (2), when the noise in popular NAS benchmark datasets is reduced to a minimum, hill-climbing to outperforms many popular state-of-the-art algorithms.
Adversarial Immunization for Certifiable Robustness on Graphs
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time.
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems
The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees and represents the state-of-the-art algorithm for a number of games.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
We introduce Policy Optimization with Multiple Optima (POMO), an end-to-end approach for building such a heuristic solver.
A Reinforcement Learning Approach to the Orienteering Problem with Time Windows
This study explores the use of Pointer Network models trained using reinforcement learning to solve the OPTW problem.
