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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 171180 of 1277 papers

TitleStatusHype
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines0
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms0
LLMOPT: Learning to Define and Solve General Optimization Problems from ScratchCode2
Initialization Method for Factorization Machine Based on Low-Rank Approximation for Constructing a Corrected Approximate Ising Model0
Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization0
Neural Solver Selection for Combinatorial OptimizationCode0
Combinatorial optimization of the coefficient of determinationCode0
D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis0
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented LearningCode0
Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints0
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