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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 81–90 of 1277 papers

TitleStatusHype
Neural Combinatorial Optimization via Preference Optimization—0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning—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
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
A2Perf: Real-World Autonomous Agents Benchmark—0
Lattice Protein Folding with Variational Annealing—0
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization—0
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