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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 10411050 of 1277 papers

TitleStatusHype
Charged particle tracking with quantum annealing-inspired optimization0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem0
Chases and Escapes, and Optimization Problems0
Cheaper and Better: Selecting Good Workers for Crowdsourcing0
Chemical Reaction Optimization for the Set Covering Problem0
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions0
Clustering Binary Data by Application of Combinatorial Optimization Heuristics0
Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology0
The Curious Case of Class Accuracy Imbalance in LLMs: Post-hoc Debiasing via Nonlinear Integer Programming0
CoCo: Learning Strategies for Online Mixed-Integer Control0
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