A Comparison of Block Pivoting and Interior-Point Algorithms for Linear Least Squares Problems with Nonnegative Variables

DOI10.2307/2153286zbMath0812.90124OpenAlexW1991870281MaRDI QIDQ4317664

Luis F. Portugal, Joaquim Joao Júdice, Luis Nunes Vicente

Publication date: 20 December 1994

Full work available at URL: https://doi.org/10.2307/2153286

zbMATH Keywords

predictor-corrector methodsblock principal pivotinglarge-scale linear least squaresnonnegative varibles

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Large-scale problems in mathematical programming (90C06) Quadratic programming (90C20)

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Uses Software

• Harwell-Boeing sparse matrix collection
• symrcm

Cites Work

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• On iterative algorithms for linear least squares problems with bound constraints
• Computational experience with a primal-dual interior point method for linear programming
• A direct method for sparse least squares problems with lower and upper bounds
• Solution of sparse linear least squares problems using Givens rotations
• Block pivot methods for solving the complementarity problem
• Sparse matrix test problems
• Bard-type Methods for the Linear Complementarity problem with symmetric positive Definite Matrices
• On the Implementation of a Primal-Dual Interior Point Method
• An Estimate for the Condition Number of a Matrix
• Higher-Order Predictor-Corrector Interior Point Methods with Application to Quadratic Objectives