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HiTSP: Towards a Hierarchical Neural Framework for Large-scale Traveling Salesman Problems
Recently, learned heuristics have been widely applied to solve combinatorial optimization problems (e.g., traveling salesman problem (TSP)). However,...
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A sequential reduction algorithm for the large-scale fixed-charge network flow problems
The fixed-charge network flow problem (FCNFP) is widely used in industrial production and can be exactly solved by converting to mixed-integer linear...
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A hybrid iterated local search matheuristic for large-scale single source capacitated facility location problems
The Single Source Capacitated Facility Location Problem (SSCFLP) consists of determining locations for facilities to meet customer demands so that...
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A split Levenberg-Marquardt method for large-scale sparse problems
We consider large-scale nonlinear least squares problems with sparse residuals, each of them depending on a small number of variables. A decoupling...
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The Regularized Block GMERR Method and Its Simpler Version for Solving Large-Scale Linear Discrete Ill-Posed Problems
Based on the block Arnoldi process and minimizing the Frobenius norm of the error, the block generalized minimal error (GMERR) method and its simpler...
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Some improved Dai–Yuan conjugate gradient methods for large-scale unconstrained optimization problems
In this paper, we introduce some modifications of the classic conjugate gradient method Dai–Yuan, to solve large-scale unconstrained optimization...
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A large-scale neighborhood search algorithm for multi-activity tour scheduling problems
In this research, we study multi-activity tour scheduling problems with heterogeneous employees in a service sector where demand varies greatly...
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A Dual Semismooth Newton Based Augmented Lagrangian Method for Large-Scale Linearly Constrained Sparse Group Square-Root Lasso Problems
Square-root Lasso problems have already be shown to be robust regression problems. Furthermore, square-root regression problems with structured...
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Solving Large-Scale Routing Optimization Problems with Networks and Only Networks
AbstractFor the first time, a fully neural approach has been proposed, capable of solving the optimization problem of routes of extremely large...
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Methods for Large-Scale Optimization
In this chapter we present methods for solving large scale nonlinear equations and nonlinear unconstrained optimization problems. In particular, we...
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Deterministic Large-Scale Decomposition Methods
In this chapter, we study decomposition methods for deterministic large-scale linear programs (LPs). These methods were developed in the 1960s and...
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An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems
We develop an inexact primal-dual first-order smoothing framework to solve a class of non-bilinear saddle point problems with primal strong...
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A class of three-term derivative-free methods for large-scale nonlinear monotone system of equations and applications to image restoration problems
In this paper, we develop a class of derivative-free methods for large-scale nonlinear monotone system of equations. They combine the hybrid...
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Inertial projected gradient method for large-scale topology optimization
We present an inertial projected gradient method for solving large-scale topology optimization problems. We consider the compliance minimization...
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An Adaptive Generalized Multiscale Finite Element Method Based Two-Grid Preconditioner for Large Scale High-Contrast Linear Elasticity Problems
In this paper, we propose an efficient and robust two-grid preconditioner for the linear elasticity equation with high contrasts. To tackle the...
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Randomized rounding algorithms for large scale unsplittable flow problems
Unsplittable flow problems cover a wide range of telecommunication and transportation problems and their efficient resolution is key to a number of...
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Provable Stochastic Algorithm for Large-Scale Fully-Connected Tensor Network Decomposition
The fully-connected tensor network (FCTN) decomposition is an emerging method for processing and analyzing higher-order tensors. For an N th-order...
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A hybrid BB-type method for solving large scale unconstrained optimization
In this paper, based on the ideas of Barzilai and Borwein (BB) method and IMPBOT algorithm proposed by Brown and Biggs (J Optim Theory Appl...
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Joint Spectral Regression Methods for Large-Scale Discriminant Analysis
Spectral regression discriminant analysis (SRDA) is one of the most popular methods for large-scale discriminant analysis. It is a stepwise algorithm...
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A semi-randomized Kaczmarz method with simple random sampling for large-scale linear systems
Randomized Kaczmarz-type methods are appealing for large-scale linear systems arising from big data problems. One of the keys of randomized...