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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 10611070 of 1277 papers

TitleStatusHype
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization0
Combinatorial Topic Models using Small-Variance Asymptotics0
Combining Learned Representations for Combinatorial Optimization0
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem0
Complex Vehicle Routing with Memory Augmented Neural Networks0
Composing photomosaic images using clustering based evolutionary programming0
Computational Protein Design Using AND/OR Branch-and-Bound Search0
Computing with Residue Numbers in High-Dimensional Representation0
Concentration of Data Encoding in Parameterized Quantum Circuits0
Concept-based Summarization using Integer Linear Programming: From Concept Pruning to Multiple Optimal Solutions0
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