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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 211220 of 1277 papers

TitleStatusHype
A novel channel pruning method for deep neural network compression0
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment0
Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization0
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem0
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning0
Annealed Training for Combinatorial Optimization on Graphs0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization0
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