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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

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Showing 261–270 of 1277 papers

TitleStatusHype
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution—0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning—0
Neural Combinatorial Optimization via Preference Optimization—0
Self-Supervised Penalty-Based Learning for Robust Constrained Optimization—0
Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver—0
Reheated Gradient-based Discrete Sampling for Combinatorial OptimizationCode0
Learning to Reduce Search Space for Generalizable Neural Routing Solver—0
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