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 731740 of 1277 papers

TitleStatusHype
Sample Complexity of Automated Mechanism Design0
Sample Selection for Fair and Robust Training0
SatNet: A Benchmark for Satellite Scheduling Optimization0
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning0
Scalable Anomaly Detection in Large Homogenous Populations0
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics0
Scalable Feature Subset Selection for Big Data using Parallel Hybrid Evolutionary Algorithm based Wrapper in Apache Spark0
Scalable iterative pruning of large language and vision models using block coordinate descent0
Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression0
Show:102550
← PrevPage 74 of 128Next →

No leaderboard results yet.