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A Global Optimization Algorithm (GOP) for Certain Classes of Nonconvex NLPs : II. Application of Theory and Test Problems
 Engng
, 1990
"... In Part I (Floudas and Visweswaran, 1990), a deterministic global optimization approach was proposed for solving certain classes of nonconvex optimization problems. An algorithm, GOP, was presented for the rigorous solution of the problem through a series of primal and relaxed dual problems until th ..."
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Cited by 61 (23 self)
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In Part I (Floudas and Visweswaran, 1990), a deterministic global optimization approach was proposed for solving certain classes of nonconvex optimization problems. An algorithm, GOP, was presented for the rigorous solution of the problem through a series of primal and relaxed dual problems until the upper and lower bounds from these problems converged to an fflglobal optimum. In this paper, theoretical results are presented for several classes of mathematical programming problems that include : (i) the general quadratic programming problem, (ii) quadratic programming problems with quadratic constraints, (iii) pooling and blending problems, and (iv) unconstrained and constrained optimization problems with polynomial terms in the objective function and/or constraints. For each class, a few examples are presented illustrating the approach. Keywords : Global Optimization, Quadratic Programming, Quadratic Constraints, Polynomial functions, Pooling and Blending Problems. Author to whom...
On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs
, 1994
"... A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes corresponding ..."
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Cited by 60 (4 self)
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A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes corresponding to stages. In both cases the method has favorable convergence properties and a structure which makes it convenient for parallel computing environments. Keywords: Stochastic Programming, Decomposition, Augmented Lagrangian, Jacobi Method, Parallel Computation. iii iv On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs Andrzej Ruszczy'nski 1 Introduction Multistage stochastic optimization problems belong to the most difficult problems of mathematical programming. Their size grows very quickly with the number of stages and with the number of events (scenarios) incorporated into the model. Although problems of this type occur frequently in applications (like,...
Global minimization using an Augmented Lagrangian method with variable lowerlevel constraints
, 2007
"... A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration k the method requires the εkglobal minimization of the Augmented Lagrangian with simple constraints, where εk → ε. Global c ..."
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Cited by 39 (1 self)
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A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration k the method requires the εkglobal minimization of the Augmented Lagrangian with simple constraints, where εk → ε. Global convergence to an εglobal minimizer of the original problem is proved. The subproblems are solved using the αBB method. Numerical experiments are presented.
A VariablePenalty Alternating Directions Method for Convex Optimization
"... We study a generalized version of the method of alternating directions as applied to the minimization of the sum of two convex functions subject to linear constraints. The method consists of solving consecutively in each iteration two optimization problems which contain in the objective function bot ..."
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Cited by 26 (0 self)
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We study a generalized version of the method of alternating directions as applied to the minimization of the sum of two convex functions subject to linear constraints. The method consists of solving consecutively in each iteration two optimization problems which contain in the objective function both Lagrangian and proximal terms. The minimizers determine the new proximal terms and a simple update of the Lagrangian terms follows. We prove a convergence theorem which extends existing results by relaxing the assumption of uniqueness of minimizers. Another novelty is that we allow penalty matrices, and these may vary per iteration. This can be beneficial in applications, since it allows additional tuning of the method to the problem and can lead to faster convergence relative to fixed penalties. As an application, we derive a decomposition scheme for block angular optimization and present computational results on a class of dual block angular problems. Keywords: parallel computing, alter...
Augmented Lagrangian Decomposition For Sparse Convex Optimization
, 1992
"... A decomposition method for largescale convex optimization problems with blockangular structure and many linking constraints is analysed. The method is based on a separable approximation of the augmented Lagrangian function. Weak global convergence of the method is proved and speed of convergence a ..."
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Cited by 15 (5 self)
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A decomposition method for largescale convex optimization problems with blockangular structure and many linking constraints is analysed. The method is based on a separable approximation of the augmented Lagrangian function. Weak global convergence of the method is proved and speed of convergence analysed. It is shown that convergence properties of the method are heavily dependent on sparsity of the linking constraints. Application to large scale linear programming and stochastic programming is discussed. Key words: LargeScale Optimization, Decomposition, Augmented Lagrangians. iii iv Augmented Lagrangian Decomposition For Sparse Convex Optimization Andrzej Ruszczy'nski 1. Introduction Rapid development of computing technology, emergence of parallel, massively parallel and distributed computing systems provides us with an increasing computing power but also creates a need for specialized approaches that can use it efficiently. The principal objective of this paper is to analys...
Deterministic Global Optimization In Design, Control, And Computational Chemistry
 IMA Volumes in Mathematics and its Applications : Large Scale Optimization with Applications, Part II
, 1997
"... . This paper presents an overview of the deterministic global optimization approaches and their applications in the areas of Process Design, Control, and Computational Chemistry. The focus is on (i) decompositionbased primal dual methods, (ii) methods for generalized geometric programming problems, ..."
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Cited by 12 (7 self)
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. This paper presents an overview of the deterministic global optimization approaches and their applications in the areas of Process Design, Control, and Computational Chemistry. The focus is on (i) decompositionbased primal dual methods, (ii) methods for generalized geometric programming problems, and (iii) global optimization methods for general nonlinear programming problems. The classes of mathematical problems that are addressed range from indefinite quadratic programming to concave programs, to quadratically constrained problems, to polynomials, to general twice continuously differentiable nonlinear optimization problems. For the majority of the presented methods nondistributed global optimization approaches are discussed with the exception of decompositionbased methods where a distributed global optimization approach is presented. 1. Background. A significant effort has been expended in the last five decades toward theoretical and algorithmic studies of applications that arise...
Separable Approximations and Decomposition Methods for the Augmented Lagrangian
, 2013
"... In this paper we study decomposition methods based on separable approximations for minimizing the augmented Lagrangian. In particular, we study and compare the Diagonal Quadratic Approximation Method (DQAM) of Mulvey and Ruszczyński [13] and the Parallel Coordinate Descent Method (PCDM) of Richtárik ..."
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Cited by 11 (0 self)
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In this paper we study decomposition methods based on separable approximations for minimizing the augmented Lagrangian. In particular, we study and compare the Diagonal Quadratic Approximation Method (DQAM) of Mulvey and Ruszczyński [13] and the Parallel Coordinate Descent Method (PCDM) of Richtárik and Takáč [23]. We show that the two methods are equivalent for feasibility problems up to the selection of a single stepsize parameter. Furthermore, we prove an improved complexity bound for PCDM under strong convexity, and show that this bound is at least 8(L ′ / ¯ L)(ω − 1) 2 times better than the best known bound for DQAM, where ω is the degree of partial separability and L ′ and ¯ L are the maximum and average of the block Lipschitz constants of the gradient of the quadratic penalty appearing in the augmented Lagrangian. 1
Alternating directions methods for the parallel solution of largescale blockstructured optimization problems
, 1995
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Part II: Future Perspective on Optimization
 25TH YEAR ISSUE ON COMPUTERS AND CHEMICAL ENGINEERING
"... Following from Part I, which presents a retrospective on optimization, we focus here on areas that are recent active research topics and are likely to strongly influence the future of optimization algorithms and formulations. First, we discuss recent developments in deterministic global optimization ..."
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Cited by 7 (0 self)
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Following from Part I, which presents a retrospective on optimization, we focus here on areas that are recent active research topics and are likely to strongly influence the future of optimization algorithms and formulations. First, we discuss recent developments in deterministic global optimization algorithms, applied to both nonlinear programs and mixed integer programs. Second, we discuss logicbased optimization and its influence in both modeling and solving mixedinteger optimization problems. Third, we discuss issues and approaches related to largescale optimization algorithms and applications. Finally, we summarize recent progress in scientific computing and software engineering as applied to optimization applications.
Recent Advances in Global Optimization for Process Synthesis, Design and Control: Enclosure of All Solutions
 Computers and Chemical Engineering
, 1999
"... Recent advances in global optimization for process synthesis, design and control are discussed. After a review of the chemical engineering contributions, we focus on the enclosure of all solutions of nonlinear constrained systems of equations. Important theoretical results are presented accompanied ..."
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Cited by 7 (0 self)
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Recent advances in global optimization for process synthesis, design and control are discussed. After a review of the chemical engineering contributions, we focus on the enclosure of all solutions of nonlinear constrained systems of equations. Important theoretical results are presented accompanied with computational studies on the enclosure of multiple steady states and all homogeneous azeotropes. 1 Introduction and Review A significant effort has been expended in the last four decades toward theoretical and algorithmic studies of applications that arise in Chemical Engineering Process Design, Process Synthesis, Process Control, as well as in Computational Chemistry and Molecular Biology. In the last decade we have experienced a dramatic growth of interest in Chemical Engineering for new methods of global optimization and their application to important engineering, as well as computational chemistry and molecular biology problems. Contributions from the chemical engineering communit...