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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 131140 of 1277 papers

TitleStatusHype
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization0
Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem0
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex NetworkCode2
Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds0
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Multi-task Representation Learning for Mixed Integer Linear ProgrammingCode0
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