Computational experience with a new class of convex underestimators: Box-constrained NLP problems

From MaRDI portal
Publication:1768613

DOI10.1023/B:JOGO.0000044768.75992.10zbMath1133.90420OpenAlexW1995275323MaRDI QIDQ1768613

Christodoulos A. Floudas, Ioannis G. Akrotirianakis

Publication date: 15 March 2005

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

Full work available at URL: https://doi.org/10.1023/b:jogo.0000044768.75992.10



Related Items

A modification of the \(\alpha \mathrm{BB}\) method for box-constrained optimization and an application to inverse kinematics, Solution to nonconvex quadratic programming with both inequality and box constraints, A review of recent advances in global optimization, Petroleum refinery optimization, Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO, Convex relaxation for solving posynomial programs, Canonical duality for box constrained nonconvex and nonsmooth optimization problems, A reformulation framework for global optimization, 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 generalization of the classical \(\alpha \)BB convex underestimation via diagonal and nondiagonal quadratic terms, Global optimality conditions for cubic minimization problems with cubic constraints, A new algorithm for box-constrained global optimization, A review of deterministic optimization methods in engineering and management, Global minimization of difference of quadratic and convex functions over box or binary constraints, Convergence rate of McCormick relaxations, Efficient Convexification Strategy for Generalized Geometric Programming Problems, On the functional form of convex underestimators for twice continuously differentiable functions, Convex underestimation for posynomial functions of positive variables, A new global optimization method for univariate constrained twice-differentiable NLP problems, Solving the canonical dual of box- and integer-constrained nonconvex quadratic programs via a deterministic direct search algorithm, Global optimality of quadratic minimization over symmetric polytopes, Continuous GRASP with a local active-set method for bound-constrained global optimization, On the efficient Gerschgorin inclusion usage in the global optimization \(\alpha\)BB method, Solutions to quadratic minimization problems with box and integer constraints, Performance of convex underestimators in a branch-and-bound framework, An extension of the \(\alpha\mathrm{BB}\)-type underestimation to linear parametric Hessian matrices, Geometric conditions for Kuhn-Tucker sufficiency of global optimality in mathematical programming, Tight convex underestimators for \({{\mathcal C}^2}\)-continuous problems. I: Univariate functions, Tight convex underestimators for \({\mathcal{C}^2}\)-continuous problems. II: Multivariate functions, A new class of improved convex underestimators for twice continuously differentiable constrained NLPs, Sufficient global optimality conditions for multi-extremal smooth minimisation problems with bounds and linear matrix inequality constraints


Uses Software