Using conical regularization in calculating Lagrangian estimates in quadratic optimization problems

DOI10.1007/s10559-017-9973-zzbMath1411.90250OpenAlexW2759830431MaRDI QIDQ681722

O. A. Berezovskyi, Yu. P. Laptin

Publication date: 13 February 2018

Published in: Cybernetics and Systems Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10559-017-9973-z

zbMATH Keywords

Lagrangian relaxationquadratic optimization problemcondition of nonnegative definiteness of matrixconical regularization

Mathematics Subject Classification ID

Quadratic programming (90C20)

Cites Work

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• Exactness criteria for SDP-relaxations of quadratic extremum problems
• An approach to determining Shor's dual quadratic estimates
• The ellipsoid method and its consequences in combinatorial optimization
• Modified \(r\)-algorithm to find the global minimum of polynomial functions
• Linear programming with positive semi-definite matrices
• Affine scaling algorithm fails for semidefinite programming
• Using the method of dual quadratic solutions to solve systems of polynomial equations in the complex domain
• Hit-and-run mixes fast
• An admissible dual internal point method for a linear semidefinite programming problem
• On Lagrangian Relaxation of Quadratic Matrix Constraints
• Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions
• A primal interior point method for the linear semidefinite programming problem
• Method of obtaining estimates in quadratic extremal problems with Boolean variables
• Cone-LP's and semidefinite programs: Geometry and a simplex-type method
• Semidefinite Programming

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