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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 101110 of 1277 papers

TitleStatusHype
Planning of Heuristics: Strategic Planning on Large Language Models with Monte Carlo Tree Search for Automating Heuristic Optimization0
GraphThought: Graph Combinatorial Optimization with Thought Generation0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models0
Self-Evaluation for Job-Shop Scheduling0
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics0
Improving Existing Optimization Algorithms with LLMs0
Currency Arbitrage Optimization using Quantum Annealing, QAOA and Constraint Mapping0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning0
Blackout DIFUSCOCode0
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set0
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