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17
A Global Optimization Method, αBB, for General TwiceDifferentiable Constrained NLPs: I  Theoretical Advances
, 1997
"... In this paper, the deterministic global optimization algorithm, αBB, (αbased Branch and Bound) is presented. This algorithm offers mathematical guarantees for convergence to a point arbitrarily close to the global minimum for the large class of twicedifferentiable NLPs. The key idea is the constru ..."
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Cited by 52 (3 self)
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In this paper, the deterministic global optimization algorithm, αBB, (αbased Branch and Bound) is presented. This algorithm offers mathematical guarantees for convergence to a point arbitrarily close to the global minimum for the large class of twicedifferentiable NLPs. The key idea is the construction of a converging sequence of upper and lower bounds on the global minimum through the convex relaxation of the original problem. This relaxation is obtained by (i) replacing all nonconvex terms of special structure (i.e., bilinear, trilinear, fractional, fractional trilinear, univariate concave) with customized tight convex lower bounding functions and (ii) by utilizing some α parameters as defined by Maranas and Floudas (1994b) to generate valid convex underestimators for nonconvex terms of generic structure. In most cases, the calculation of appropriate values for the α parameters is a challenging task. A number of approaches are proposed, which rigorously generate a set of α par...
Global Optimization in Parameter Estimation of Nonlinear Algebraic Models via the ErrorInVariables Approach
 Ind. Eng. Chem. Res
, 1998
"... The estimation of parameters in nonlinear algebraic models through the errorinvariables method has been widely studied from a computational standpoint. The method involves the minimization of a weighted sum of squared errors subject to the model equations. Due to the nonlinear nature of the models ..."
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Cited by 17 (3 self)
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The estimation of parameters in nonlinear algebraic models through the errorinvariables method has been widely studied from a computational standpoint. The method involves the minimization of a weighted sum of squared errors subject to the model equations. Due to the nonlinear nature of the models used, the resulting formulation is nonconvex, and may contain several local minima in the region of interest. Current methods tailored for this formulation, although computationally efficient, can only attain convergence to a local solution. In this paper, a global optimization approach based on a branch and bound framework and convexification techniques for general twice differentiable nonlinear optimization problems is proposed for the parameter estimation of nonlinear algebraic models. The proposed convexification techniques exploit the mathematical properties of the formulation. Classical nonlinear estimation problems were solved and will be used to illustrate the various theoretical an...
Global Optimization of MINLP Problems in Process Synthesis and Design
 Computers & Chemical Engineering
, 1997
"... : Two new methodologies for the global optimization of MINLP models, the Special structure Mixed Integer Nonlinear ffBB, SMINffBB, and the General structure Mixed Integer Nonlinear ffBB, GMINffBB, are presented. Their theoretical foundations provide guarantees that the global optimum solution of ..."
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Cited by 15 (6 self)
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: Two new methodologies for the global optimization of MINLP models, the Special structure Mixed Integer Nonlinear ffBB, SMINffBB, and the General structure Mixed Integer Nonlinear ffBB, GMINffBB, are presented. Their theoretical foundations provide guarantees that the global optimum solution of MINLPs involving twicedifferentiable nonconvex functions in the continuous variables can be identified. The conditions imposed on the functionality of the binary variables differ for each method : linear and mixed bilinear terms can be treated with the SMINffBB; mixed nonlinear terms whose continuous relaxation is twicedifferentiable are handled by the GMINffBB. While both algorithms use the concept of a branch & bound tree, they rely on fundamentally different bounding and branching strategies. In the GMINffBB algorithm, lower (upper) bounds at each node result from the solution of convex (nonconvex) MINLPs derived from the original problem. The construction of convex lower bound...
Global Optimization of MixedInteger Nonlinear Problems
 AIChE J
"... Two novel deterministic global optimization algorithms for nonconvex mixedinteger problems (MINLPs) are proposed, using the advances of the ffBB algorithm for nonconvex NLPs Adjiman et al. (1998a). The Special Structure MixedInteger ffBB algorithm (SMINffBB addresses problems with nonconvexities ..."
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Cited by 14 (2 self)
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Two novel deterministic global optimization algorithms for nonconvex mixedinteger problems (MINLPs) are proposed, using the advances of the ffBB algorithm for nonconvex NLPs Adjiman et al. (1998a). The Special Structure MixedInteger ffBB algorithm (SMINffBB addresses problems with nonconvexities in the continuous variables and linear and mixedbilinear participation of the binary variables. The General Structure MixedInteger ffBB algorithm (GMINffBB), is applicable to a very general class of problems for which the continuous relaxation is twice continuously differentiable. Both algorithms are developed using the concepts of branchandbound, but they differ in their approach to each of the required steps. The SMINffBB algorithm is based on the convex underestimation of the continuous functions while the GMINffBB algorithm is centered around the convex relaxation of the entire problem. Both algorithms rely on optimization or interval based variable bound updates to enhance effici...
Global Optimization Approaches in Protein Folding and Peptide Docking
, 1999
"... . The recent advances in genetic engineering, high powered computing and global optimization continue to stimulate interest in the area of molecular modeling and protein structure prediction. The goal of these efforts is the ability to correctly predict native protein conformations and the binding i ..."
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Cited by 8 (2 self)
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. The recent advances in genetic engineering, high powered computing and global optimization continue to stimulate interest in the area of molecular modeling and protein structure prediction. The goal of these efforts is the ability to correctly predict native protein conformations and the binding interactions of macromolecules. These two problems currently dominate the field of computational chemistry and, through the use of detailed molecular models, they have also greatly influenced research in the area of global optimization. This article examines some aspects related to conformational energy modeling and reviews a variety of global optimization approaches developed for the protein folding and peptide docking problems. 1. Introduction Proteins are undoubtedly the most complex and vital molecules in nature. This complexity arises from an intricate balance of intra and intermolecular interactions which define the native threedimensional structure of the system, and subsequently i...
Predicting Solvated Peptide Conformations via Global Minimization of Energetic AtomtoAtom Interactions
 Comput. Chem. Eng
, 1997
"... A global optimization method is described for identifying the global minimum energy conformation, as well as lower and upper bounds on the global minimum conformer of solvated peptides. Potential energy contributions are calculated using the ECEPP/3 force field model. In considering the effects of h ..."
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Cited by 7 (6 self)
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A global optimization method is described for identifying the global minimum energy conformation, as well as lower and upper bounds on the global minimum conformer of solvated peptides. Potential energy contributions are calculated using the ECEPP/3 force field model. In considering the effects of hydration, two implicit free energy models are compared. One method is based on the calculation of solventaccessible surface areas, while the other uses information on the solventaccessible volume of hydration shells. Detailed information on the potential and solvation energy contributions is presented for the terminally blocked single residue peptides. In addition, based on a procedure that allows the exclusion of domains of the (OE, /) space, a number of oligopeptide structure prediction problems are considered, and the role of the solvation model in defining global minimum conformations is addressed. Keywords : Protein folding, Solvation, Global optimization. Current Address: Corpo...
MixedInteger Nonlinear Optimization in Process Synthesis
, 1998
"... The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the process synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw ma ..."
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Cited by 7 (0 self)
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The use of networks allows the representation of a variety of important engineering problems. The treatment of a particular class of network applications, the process synthesis problem, is exposed in this paper. Process Synthesis seeks to develop systematically process flowsheets that convert raw materials into desired products. In recent years, the optimization approach to process synthesis has shown promise in tackling this challenge. It requires the development of a network of interconnected units, the process superstructure, that represents the alternative process flowsheets. The mathematical modeling of the superstructure has a mixed set of binary and continuous variables and results in a mixedinteger optimization model. Due to the nonlinearity of chemical models, these problems are generally classified as MixedInteger Nonlinear Programming (MINLP) problems. A number of local optimization algorithms, developed for the solution of this class of problems, are presented in this pap...
Phase Stability with Cubic Equations of State: A Global Optimization Approach
 AIChE J
, 2000
"... Calculation of phase and chemical equilibria is of fundamental importance for the design and simulation of chemical processes. Methods that minimize the Gibbs free energy provide equilibrium solutions that are only candidates for the true equilibrium solution. This is because the number and type of ..."
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Cited by 7 (0 self)
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Calculation of phase and chemical equilibria is of fundamental importance for the design and simulation of chemical processes. Methods that minimize the Gibbs free energy provide equilibrium solutions that are only candidates for the true equilibrium solution. This is because the number and type of phases must be assumed before the Gibbs energy minimization problem can be formulated. The tangent plane stability criterion is a means of determining the stability of a candidate equilibrium solution. The Gibbs energy minimization problem and the tangent plane stability problem are very challenging due to the highly nonlinear thermodynamic functions that are used. In this work the goal is to develop a global optimization approach for the tangent plane stability problem that (i) provides a theoretical guarantee about the stability of the candidate equilibrium solution and (ii) is computationally efficient. Cubic equations of state are used in this approach due to their ability to accurately ...
Comparison of Deterministic and Stochastic Approaches to global optimization
"... In this paper we compare two different approaches to nonconvex global optimization. The first one is a deterministic spatial BranchandBound algorithm (sBB), whereas the second approach is a quasi Monte Carlo (QMC) variant of a stochastic multi level single linkage (MLSL) algorithm. Both algorithms ..."
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Cited by 6 (3 self)
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In this paper we compare two different approaches to nonconvex global optimization. The first one is a deterministic spatial BranchandBound algorithm (sBB), whereas the second approach is a quasi Monte Carlo (QMC) variant of a stochastic multi level single linkage (MLSL) algorithm. Both algorithms apply to problems in a very general form and are not dependent on problem structure. The test suite we chose is fairly extensive in scope, in that it includes constrained and unconstrained problems, continuous and mixedinteger problems. The conclusion of the tests is that in general the QMC variant of the MLSL algorithm is more efficient, although in some instances the BranchandBound algorithm is capable of locating the global optimum of hard problems in just one iteration.
Distributed Branch And Bound Algorithms For Global Optimization
 IMA Volumes in Mathematics and its Applications, Vol. 106. Parallel Processing of Discrete Problems
, 1998
"... . This paper presents computational results of the parallelized version of the ffBB global optimization algorithm. Important algorithmic and implementational issues are discussed and their impact on the design of parallel branch and bound methods is analyzed. These issues include selection of the ap ..."
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Cited by 5 (0 self)
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. This paper presents computational results of the parallelized version of the ffBB global optimization algorithm. Important algorithmic and implementational issues are discussed and their impact on the design of parallel branch and bound methods is analyzed. These issues include selection of the appropriate architecture, communication patterns, frequency of communication, and termination detection. The approach is demonstrated with a variety of computational studies aiming at revealing the various types of behavior of the distributed branch and bound global optimization algorithm can exhibit. These include ideal behavior, speedup, detrimental, and deceleration anomalies. Key words. Global optimization, parallel computing, branch and bound. 1. Introduction. A wide range of optimal selection problems, in diverse scientific areas, can be formulated as non linear constrained optimization problems. One of the most common characteristics of these problems is the presence of nonconvexitie...