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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 851–860 of 1277 papers

TitleStatusHype
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning—0
Two-Dimensional Phase Unwrapping via Balanced Spanning Forests—0
Two-Stage Learning For the Flexible Job Shop Scheduling Problem—0
UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning—0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization—0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems—0
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set—0
Unsupervised Learning for Quadratic Assignment—0
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement—0
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?—0
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