Quadratic assignment problem
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Fast Approximate Quadratic Programming for Graph Matching Open
Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment proble…
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Application of Ising Machines and a Software Development for Ising Machines Open
An online advertisement optimization, which can be represented by a combinatorial optimization problem is performed using D-Wave 2000Q, a quantum annealing machine. To optimize the online advertisement allocation optimization, we introduce…
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Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization Open
Combinatorial optimization has found applications in numerous fields, from aerospace to transportation planning and economics. The goal is to find an optimal solution among a finite set of possibilities. The well-known challenge one faces …
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A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. Open
Inverse problems correspond to a certain type of optimization problems formulated over appropriate input distributions. Recently, there has been a growing interest in understanding the computational hardness of these optimization problems,…
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Deep Neural Networks for Linear Sum Assignment Problems Open
Many resource allocation issues in wireless communications can be modeled as assignment problems and can be solved online with global information. However, traditional methods for assignment problems take a lot of time to find the optimal …
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An Hybrid Fuzzy Variable Neighborhood Particle Swarm Optimization Algorithm for Solving Quadratic Assignment Problems Open
Recently, Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. A quick review of the literature reveals that research for solving the Quadratic Assignment Problem (QAP) usi…
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Unconstrained binary models of the travelling salesman problem variants for quantum optimization Open
Quantum computing is offering a novel perspective for solving combinatorial optimization problems. To fully explore the possibilities offered by quantum computers, the problems need to be formulated as unconstrained binary models, taking i…
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A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem Open
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, …
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Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies Open
Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the…
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Data-Driven Approximations to NP-Hard Problems Open
There exist a number of problem classes for which obtaining the exact solution becomes exponentially expensive with increasing problem size. The quadratic assignment problem (QAP) or the travelling salesman problem (TSP) are just two examp…
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Annealing Ant Colony Optimization with Mutation Operator for Solving TSP Open
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks o…
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BiqCrunch Open
This article presents BiqCrunch , an exact solver for binary quadratic optimization problems. BiqCrunch is a branch-and-bound method that uses an original, efficient semidefinite-optimization-based bounding procedure. It has been successfu…
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Solving the Traveling Salesman Problem on the D-Wave Quantum Computer Open
The traveling salesman problem is a well-known NP-hard problem in combinatorial optimization. This paper shows how to solve it on an Ising Hamiltonian based quantum annealer by casting it as a quadratic unconstrained binary optimization (Q…
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A Parallel Simulated Annealing Algorithm for Weapon-Target Assignment Problem Open
Weapon-target assignment (WTA) is a combinatorial optimization problem and is known to be NP-complete. The WTA aims to best assignment of weapons to targets to minimize the total expected value of the surviving targets. Exact methods can s…
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Memetic Search for the Generalized Quadratic Multiple Knapsack Problem Open
The generalized quadratic multiple knapsack problem (GQMKP) extends the classical quadratic multiple knapsack problem with setups and knapsack preference of the items. The GQMKP can accommodate a number of real-life applications and is com…
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Hospital layout design renovation as a Quadratic Assignment Problem with geodesic distances Open
Hospital facilities are known as functionally complex buildings. There are usually configurational problems that lead to inefficient transportation processes for patients, medical staff, and/or logistics of materials. The Quadratic Assignm…
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Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment Open
Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, wher…
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Approximate Graph Edit Distance by Several Local Searches in Parallel Open
Solving or approximating the linear sum assignment problem (LSAP) is an important step of several constructive and local search strategies developed to approximate the graph edit distance (GED) of two attributed graphs, or more generally t…
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A QUBO Formulation of Minimum Multicut Problem Instances in Trees for D-Wave Quantum Annealers Open
Quantum annealing algorithms were introduced to solve combinatorial optimization problems by taking advantage of quantum fluctuations to escape local minima in complex energy landscapes typical of NP − hard problems. In this work, we propo…
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Sliced Gromov-Wasserstein Open
Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space. However, this Optimal Transport (OT) distance requi…
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On the emerging potential of quantum annealing hardware for combinatorial optimization Open
Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not prov…
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Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching Open
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This paper presents a QAP network directly learning with the affini…
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Quantum-Inspired Evolutionary Approach for the Quadratic Assignment Problem Open
The paper focuses on the opportunity of the application of the quantum-inspired evolutionary algorithm for determining minimal costs of the assignment in the quadratic assignment problem. The idea behind the paper is to present how the alg…
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Graph edit distance as a quadratic program Open
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A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem Open
In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with th…
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On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization Open
Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not prov…
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Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model Open
Graph matching aims at finding the vertex correspondence between two unlabeled graphs that maximizes the total edge weight correlation. This amounts to solving a computationally intractable quadratic assignment problem. In this paper we pr…
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Nonnegative Orthogonal Graph Matching Open
Graph matching problem that incorporates pair-wise constraints can be formulated as Quadratic Assignment Problem(QAP). The optimal solution of QAP is discrete and combinational, which makes QAP problem NP-hard. Thus, many algorithms have b…
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Larger Sparse Quadratic Assignment Problem Optimization Using Quantum Annealing and a Bit-Flip Heuristic Algorithm Open
Quantum annealing and D-Wave quantum annealer attracted considerable\nattention for their ability to solve combinatorial optimization problems. In\norder to solve other type of optimization problems, it is necessary to apply\ncertain kinds…
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Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks Open
Minimum weighted vertex cover is the NP-hard graph problem of choosing a subset of vertices incident to all edges such that the sum of the weights of the chosen vertices is minimum. Previous efforts for solving this in practice have typica…