Journal ArticleDOI
Global optimization in generalized geometric programming
TLDR
The proposed branch and bound type algorithm attains finite ϵ-convergence to the global minimum through the successive refinement of a convex relaxation of the feasible region and/or of the objective function and the subsequent solution of a series of nonlinear convex optimization problems.About:
This article is published in Computers & Chemical Engineering.The article was published on 1997-12-20. It has received 218 citations till now. The article focuses on the topics: Nonlinear programming & Convex optimization.read more
Citations
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Journal ArticleDOI
A tutorial on geometric programming
TL;DR: This tutorial paper collects together in one place the basic background material needed to do GP modeling, and shows how to recognize functions and problems compatible with GP, and how to approximate functions or data in a formcompatible with GP.
Journal ArticleDOI
Variations and extension of the convex–concave procedure
Thomas Lipp,Stephen Boyd +1 more
TL;DR: This work investigates the convex–concave procedure, a local heuristic that utilizes the tools of convex optimization to find local optima of difference of conveX (DC) programming problems, and generalizes the algorithm to include vector inequalities.
Book
Geometric Programming for Communication Systems
TL;DR: This text provides both an in-depth tutorial on the theory, algorithms, and modeling methods of GP, and a comprehensive survey on the applications of GP to the study of communication systems.
Journal ArticleDOI
A global optimization method, αBB, for general twice-differentiable constrained NLPs — I. Theoretical advances
TL;DR: The deterministic global optimization algorithm, αBB (α-based Branch and Bound) is presented, which offers mathematical guarantees for convergence to a point arbitrarily close to the global minimum for the large class of twice-differentiable NLPs.
References
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Book
Global Optimization: Deterministic Approaches
Reiner Horst,Tuy Hoang +1 more
TL;DR: This study develops a unifying approach to constrained global optimization that provides insight into the underlying concepts and properties of diverse techniques recently proposed to solve a wide variety of problems encountered in the decision sciences, engineering, operations research and other disciplines.
Journal ArticleDOI
Global optimization of nonconvex NLPs and MINLPs with applications in process design
TL;DR: Computational results demonstrate that the algorithm compares very favorably to several other current approaches when applied to a large collection of global optimization and process design problems, typically faster, requires less storage and it produces more accurate results.