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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 241250 of 1277 papers

TitleStatusHype
Unraveling the Rainbow: can value-based methods schedule?Code0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints0
Learning to Learn with Quantum Optimization via Quantum Neural Networks0
QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks0
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search0
Application of the Brain Drain Optimization Algorithm to the N-Queens Problem0
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis0
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling0
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution0
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