A research protocol for deep graph matching.
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Updated
Jul 30, 2024 - Python
A research protocol for deep graph matching.
Python library for finding the optimal transformation(s) that makes two matrices as close as possible to each other.
Solution quadratic assign problem via LS(local search), ILS(iterated local search), GLS(guided local search)
The research work on local search algorithms
This is a Python implementation of the Doubly Stochastic Projected Fixed Point (DSPFP) algorithm for solving the Quadratic Assignment Problem / Graph Matching..
Algorithms for solving the Quadratic Assignment Problem
Implementation of different algorithms: heuristic search, Simulated Annealing, Ant Colony Algorithm and Tabu Search to a Quadratic Assignment Problem.
Unofficial implemnetation of "Solving Quadratic Assignemt Problem using Deep Reinforcement Learning" (https://arxiv.org/abs/2310.01604)
Algorithms for solving the quadratic assignment problem (QAP) implemented in Julia.
This package supports general, orthogonal, rotation, permutation, projection, and symmetric Procrustes problems, including both the normal one-sided approach and (for orthogonal and permutation Procrustes) two-sided approaches, where both the rows and columns are transformed.
Heuristics for the Quadratic Assignment Problem (QAP) - R package
laboratory works for combinatoric optimization course
Mapping and routing of a data flow network mapped on an mesh NoC
Quadratic Assignment Problem, an Enhanced Solver
Solving quadratic assignment problem using iterated local search, improved hybrid genetic algorithm, tabu search, and constraint solving.
A simple Quadratic Assignment Problem solver using heuristics and metaheuristics
Applications of Metaheuristic Optimization in Python
Solving a QAP instance using genetic algorithms
This is the official code for the AISTATS 2023 paper "Learning Constrained Structured Spaces with Application to Multi-Graph Matching"
This repository utilizes social network analysis (SNA) to analyze multiple social networks. Clustering algorithms were used to explore sub-groups and QAP was implemented for non-parametric multiple regression analysis.
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