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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 201210 of 1277 papers

TitleStatusHype
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions0
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy0
HyColor: An Efficient Heuristic Algorithm for Graph Coloring0
Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)0
Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLMCode0
Latent Guided Sampling for Combinatorial OptimizationCode0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization0
Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search0
Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization0
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