SOTAVerified

Combinatorial Optimization

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.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 411420 of 1277 papers

TitleStatusHype
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling0
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation0
VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs0
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachCode1
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs0
Route Planning Using Nature-Inspired Algorithms0
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates0
Show:102550
← PrevPage 42 of 128Next →

No leaderboard results yet.