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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 191200 of 1277 papers

TitleStatusHype
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling0
Diversity-Driven View Subset Selection for Indoor Novel View SynthesisCode0
Deep Generative Model for Mechanical System Configuration Design0
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
Parallel AutoRegressive Models for Multi-Agent Combinatorial OptimizationCode1
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning0
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based SamplingCode0
A GREAT Architecture for Edge-Based Graph Problems Like TSPCode0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization0
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