A Newton-bracketing method for a simple conic optimization problem

DOI10.1080/10556788.2020.1782906zbMath1470.90063arXiv1905.12840OpenAlexW3039303289MaRDI QIDQ4999334

Sunyoung Kim, Kim-Chuan Toh, Kojima, Masakazu

Publication date: 6 July 2021

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.12840

zbMATH Keywords

robust numerical algorithmsconic relaxationsNewton-bracketing methodnonconvex quadratic optimization problemssecant-bracketing method for generating valid bounds

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Quadratic programming (90C20)

Related Items

Isotonicity of the proximity operator and stochastic optimization problems in Hilbert quasi-lattices endowed with Lorentz cones

Uses Software

• QAPLIB
• DSDP5
• Biq Mac
• BiqMac
• SDPNAL+
• BBCPOP

Cites Work

• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
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• SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints
• A robust Lagrangian-DNN method for a class of quadratic optimization problems
• Handbook on semidefinite, conic and polynomial optimization
• Binary quadratic optimization problems that are difficult to solve by conic relaxations
• Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems
• On the copositive representation of binary and continuous nonconvex quadratic programs
• Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
• Semidefinite Programming
• Algorithm 996
• Handbook of semidefinite programming. Theory, algorithms, and applications
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