New methods for calculating \(\alpha\)BB-type underestimators
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Publication:742131
DOI10.1007/s10898-013-0057-yzbMath1305.90337OpenAlexW1965293593MaRDI QIDQ742131
Anders Skjäl, Tapio Westerlund
Publication date: 18 September 2014
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-013-0057-y
Related Items (12)
A modification of the \(\alpha \mathrm{BB}\) method for box-constrained optimization and an application to inverse kinematics ⋮ Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO ⋮ An edge-concave underestimator for the global optimization of twice-differentiable nonconvex problems ⋮ Arbitrarily tight \(\alpha \mathrm{BB}\) underestimators of general non-linear functions over sub-optimal domains ⋮ Tighter \(\alpha \mathrm{BB}\) relaxations through a refinement scheme for the scaled Gerschgorin theorem ⋮ A rigorous deterministic global optimization approach for the derivation of secondary information in digital maps ⋮ Enclosure of all index-1 saddle points of general nonlinear functions ⋮ On the efficient Gerschgorin inclusion usage in the global optimization \(\alpha\)BB method ⋮ Linear interval parametric approach to testing pseudoconvexity ⋮ Testing pseudoconvexity via interval computation ⋮ Performance of convex underestimators in a branch-and-bound framework ⋮ An extension of the \(\alpha\mathrm{BB}\)-type underestimation to linear parametric Hessian matrices
Uses Software
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