A global optimization method, QBB, for twice-differentiable nonconvex optimization problem

DOI10.1007/s10898-005-0936-yzbMath1093.90090OpenAlexW2021381008MaRDI QIDQ811896

Yushan Zhu, Takahito Kuno

Publication date: 23 January 2006

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-005-0936-y

zbMATH Keywords

branch-and-bound algorithmGlobal optimizationinterval Hessian matrixQBBsimplicial division

Mathematics Subject Classification ID

Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30)

Related Items (4)

A practical but rigorous approach to sum-of-ratios optimization in geometric applications ⋮ A new technique to derive tight convex underestimators (sometimes envelopes) ⋮ A lagrangean based branch-and-cut algorithm for global optimization of nonconvex mixed-integer nonlinear programs with decomposable structures ⋮ On the efficient Gerschgorin inclusion usage in the global optimization \(\alpha\)BB method

Uses Software

• INTOPT_90

Cites Work

• Global minimum potential energy conformations of small molecules
• Primal-relaxed dual global optimization approach
• Decomposition based and branch and bound global optimization approaches for the phase equilibrium problem
• Convex extensions and envelopes of lower semi-continuous functions
• Global optimization of multiplicative programs
• The use of Hestenes' method of multipliers to resolve dual gaps in engineering system optimization
• Global optimization of mixed-integer nonlinear programs: a theoretical and computational study
• A branch-and-reduce approach to global optimization
• BARON: A general purpose global optimization software package
• Second-order sufficient optimality conditions for local and global nonlinear programming
• Jointly Constrained Biconvex Programming
• Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems
• Semidefinite relaxations of fractional programs via novel convexification techniques

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