On optimal control of a sweeping process coupled with an ordinary differential equation
- Authors: Adam, Lukas , Outrata, Jiri
- Date: 2014
- Type: Text , Journal article
- Relation: Discrete and Continuous Dynamical Systems - Series B Vol. 19, no. 9 (November 2014 2014), p. 2709-2738
- Full Text: false
- Reviewed:
- Description: We study a special case of an optimal control problem governed by a differential equation and a differential rate{independent variational inequality, both with given initial conditions. Under certain conditions, the variational inequality can be reformulated as a differential inclusion with discontinuous right-hand side. This inclusion is known as sweeping process. We perform a discretization scheme and prove the convergence of optimal solutions of the discretized problems to the optimal solution of the original problem. For the discretized problems we study the properties of the solution map and compute its coderivative. Employing an appropriate chain rule, this enables us to compute the subdifferential of the objective function and to apply a suitable optimization technique to solve the discretized problems. The investigated problem is used to model a situation arising in the area of queuing theory.
B-convex sets and functions
- Authors: Adilov, G. , Rubinov, Alex
- Date: 2006
- Type: Text , Journal article
- Relation: Numerical Functional Analysis and Optimization Vol. 27, no. 3-4 (Apr-May 2006), p. 237-257
- Full Text: false
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- Description: A subset B of R-+(n) is B-convex if for all x, y is an element of B and all t is an element of [0, 1] one has max (tx, y) is an element of B. These sets were first investigated in [1, 2]. In this paper, we examine radiant B-convex sets and also introduce and study B-convex functions.
- Description: C1
- Description: 2003001836
Nonsmooth Lyapunov pairs for infinite-dimensional first-order differential inclusions
- Authors: Adly, Samir , Hantoute, Abderrahim , Théra, Michel
- Date: 2012
- Type: Text , Journal article
- Relation: Nonlinear Analysis: Theory, Methods & Applications Vol. 75, no. 3 (February, 2012), p. 985-1008
- Full Text: false
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- Description: The main objective of this paper is to provide new explicit criteria to characterize weak lower semicontinuous Lyapunov pairs or functions associated to first-order differential inclusions in Hilbert spaces. These inclusions are governed by a Lipschitzian perturbation of a maximally monotone operator. The dual criteria we give are expressed by means of the proximal and basic subdifferentials of the nominal functions while primal conditions are described in terms of the contingent directional derivative. We also propose a unifying review of many other criteria given in the literature. Our approach is based on advanced tools of variational analysis and generalized differentiation.
Marginal longitudinal semiparametric regression via penalized splines
- Authors: Ali-Alkadiri, Mohammad , Carroll, R.J. , Wand, M.P.
- Date: 2011
- Type: Text , Journal article
- Relation: Statistics and Probablitity Letters Vol. 80, no. 15-16 (2011), p. 1242-1252
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- Description: We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
On Holder continuity of solution maps of parametric primal and dual Ky Fan inequalities
- Authors: Anh, Lam Quoc , Khanh, Phan Quoc , Tam, T. N.
- Date: 2015
- Type: Text , Journal article
- Relation: Top Vol. 23, no. 1 (2015), p. 151-167
- Full Text: false
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- Description: We consider parametric primal and dual Ky Fan inequalities in metric linear spaces. Sufficient conditions for Holder continuity of solutions are established. Many examples are provided to illustrate the essentialness of the imposed assumptions and advantages of the results over existing ones. As applications, we derive this Holder continuity of solutions for constrained minimization and variational inequalities.
Comparative study of RPSALG algorithm for convex semi-infinite programming
- Authors: Auslender, Alfred , Ferrer, Albert , Goberna, Miguel , López, Marco
- Date: 2014
- Type: Text , Journal article
- Relation: Computational Optimization and Applications Vol. 60, no. 1 (2014), p. 59-87
- Full Text: false
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- Description: The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of auxiliary optimization problems: the first one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement different variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.
A quasisecant method for minimizing nonsmooth functions
- Authors: Bagirov, Adil , Ganjehlou, Asef Nazari
- Date: 2010
- Type: Text , Journal article
- Relation: Optimization Methods and Software Vol. 25, no. 1 (2010), p. 3-18
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Reviewed:
- Description: We present an algorithm to locally minimize nonsmooth, nonconvex functions. In order to find descent directions, the notion of quasisecants, introduced in this paper, is applied. We prove that the algorithm converges to Clarke stationary points. Numerical results are presented demonstrating the applicability of the proposed algorithm to a wide variety of nonsmooth, nonconvex optimization problems. We also compare the proposed algorithm with the bundle method using numerical results.
Alexander Rubinov - An outstanding scholar
- Authors: Bagirov, Adil
- Date: 2010
- Type: Text , Journal article
- Relation: Pacific Journal of Optimization Vol. 6, no. 2, Suppl. 1 (2010), p. 203-209
- Full Text: false
Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Authors: Bagirov, Adil , Taheri, Sona , Cimen, Emre
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computational and Applied Mathematics Vol. 389, no. (2021), p. 1-17
- Relation: https://purl.org/au-research/grants/arc/DP190100580
- Full Text: false
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- Description: The objective function in the nonsmooth optimization model of the clusterwise linear regression (CLR) problem with the squared regression error is represented as a difference of two convex functions. Then using the difference of convex algorithm (DCA) approach the CLR problem is replaced by the sequence of smooth unconstrained optimization subproblems. A new algorithm based on the DCA and the incremental approach is designed to solve the CLR problem. We apply the Quasi-Newton method to solve the subproblems. The proposed algorithm is evaluated using several synthetic and real-world data sets for regression and compared with other algorithms for CLR. Results demonstrate that the DCA based algorithm is efficient for solving CLR problems with the large number of data points and in particular, outperforms other algorithms when the number of input variables is small. © 2020 Elsevier B.V.
An algorithm for clustering based on non-smooth optimization techniques
- Authors: Bagirov, Adil , Rubinov, Alex , Sukhorukova, Nadezda , Yearwood, John
- Date: 2003
- Type: Text , Journal article
- Relation: International Transactions in Operational Research Vol. 10, no. 6 (2003), p. 611-617
- Full Text: false
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- Description: The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, and an algorithm for solving the cluster analysis problem based on non-smooth optimization techniques is developed. We discuss applications of this algorithm in large databases. Results of numerical experiments are presented to demonstrate the effectiveness of this algorithm.
- Description: C1
- Description: 2003000422
A new nonsmooth optimization algorithm for minimum sum-of-squares clustering problems
- Authors: Bagirov, Adil , Yearwood, John
- Date: 2006
- Type: Text , Journal article
- Relation: European Journal of Operational Research Vol. 170, no. 2 (2006), p. 578-596
- Full Text: false
- Reviewed:
- Description: The minimum sum-of-squares clustering problem is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the former problem based on nonsmooth optimization techniques is developed. The issue of applying this algorithm to large data sets is discussed. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithm. © 2004 Elsevier B.V. All rights reserved.
- Description: C1
- Description: 2003001520
Codifferential method for minimizing nonsmooth DC functions
- Authors: Bagirov, Adil , Ugon, Julien
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 50, no. 1 (2011), p. 3-22
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Reviewed:
- Description: In this paper, a new algorithm to locally minimize nonsmooth functions represented as a difference of two convex functions (DC functions) is proposed. The algorithm is based on the concept of codifferential. It is assumed that DC decomposition of the objective function is known a priori. We develop an algorithm to compute descent directions using a few elements from codifferential. The convergence of the minimization algorithm is studied and its comparison with different versions of the bundle methods using results of numerical experiments is given. © 2010 Springer Science+Business Media, LLC.
An algorithm for clusterwise linear regression based on smoothing techniques
- Authors: Bagirov, Adil , Ugon, Julien , Mirzayeva, Hijran
- Date: 2014
- Type: Text , Journal article
- Relation: Optimization Letters Vol. 9, no. 2 (2014), p. 375-390
- Full Text: false
- Reviewed:
- Description: We propose an algorithm based on an incremental approach and smoothing techniques to solve clusterwise linear regression (CLR) problems. This algorithm incrementally divides the whole data set into groups which can be easily approximated by one linear regression function. A special procedure is introduced to generate an initial solution for solving global optimization problems at each iteration of the incremental algorithm. Such an approach allows one to find global or approximate global solutions to the CLR problems. The algorithm is tested using several data sets for regression analysis and compared with the multistart and incremental Spath algorithms.
Comparison of metaheuristic algorithms for pump operation optimization
- Authors: Bagirov, Adil , Ahmed, S. T. , Barton, Andrew , Mala-Jetmarova, Helena , Al Nuaimat, Alia , Sultanova, Nargiz
- Date: 2012
- Type: Text , Conference paper
- Relation: 14th Water Distribution Systems Analysis Conference 2012, WDSA 2012 Vol. 2; Adelaide, Australia; 24th-27th September 2012; p. 886-896
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
- Reviewed:
- Description: Pumping cost constitutes the main part of the overall operating cost of water distribution systems. There are different optimization formulations of the pumping cost minimization problem including those with application of continuous and integer programming approaches. To date mainly various metaheuristics have been applied to solve this problem. However, the comprehensive comparison of those metaheuristics has not been done. Such a comparison is important to identify strengths and weaknesses of different algorithms which reflects on their performance. In this paper, we present a methodology for comparative analysis of widely used metaheuristics for solving the pumping cost minimization problem. This methodology includes the following comparison criteria: (a) the "optimal solution" obtained; (b) the efficiency; and (c) robustness. Algorithms applied are: particle swarm optimization, artificial bee colony and firefly algorithms. These algorithms were applied to one test problem available in the literature. The results obtained demonstrate that the artificial bee colony is the most robust and the firefly is the most efficient and accurate algorithm for this test problem. Funding :ARC
Preface of the special issue OR: Connecting sciences supported by global optimization related to the 25th European conference on operational research (EURO XXV 2012)
- Authors: Bagirov, Adil , Miettinen, Kaisa , Weber, Gerhard-Wilhelm
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 60, no. 1 (June 2014), p. 1-3
- Full Text: false
- Reviewed:
- Description: C1
Classification through incremental max-min separability
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean , Karasozen, Bulent
- Date: 2011
- Type: Text , Journal article
- Relation: Pattern Analysis and Applications Vol. 14, no. 2 (2011), p. 165-174
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Reviewed:
- Description: Piecewise linear functions can be used to approximate non-linear decision boundaries between pattern classes. Piecewise linear boundaries are known to provide efficient real-time classifiers. However, they require a long training time. Finding piecewise linear boundaries between sets is a difficult optimization problem. Most approaches use heuristics to avoid solving this problem, which may lead to suboptimal piecewise linear boundaries. In this paper, we propose an algorithm for globally training hyperplanes using an incremental approach. Such an approach allows one to find a near global minimizer of the classification error function and to compute as few hyperplanes as needed for separating sets. We apply this algorithm for solving supervised data classification problems and report the results of numerical experiments on real-world data sets. These results demonstrate that the new algorithm requires a reasonable training time and its test set accuracy is consistently good on most data sets compared with mainstream classifiers. © 2010 Springer-Verlag London Limited.
An algorithm for minimization of pumping costs in water distribution systems using a novel approach to pump scheduling
- Authors: Bagirov, Adil , Barton, Andrew , Mala-Jetmarova, Helena , Al Nuaimat, Alia , Ahmed, S. T. , Sultanova, Nargiz , Yearwood, John
- Date: 2013
- Type: Text , Journal article
- Relation: Mathematical and Computer Modelling Vol. 57, no. 3-4 (2013), p. 873-886
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
- Reviewed:
- Description: The operation of a water distribution system is a complex task which involves scheduling of pumps, regulating water levels of storages, and providing satisfactory water quality to customers at required flow and pressure. Pump scheduling is one of the most important tasks of the operation of a water distribution system as it represents the major part of its operating costs. In this paper, a novel approach for modeling of explicit pump scheduling to minimize energy consumption by pumps is introduced which uses the pump start/end run times as continuous variables, and binary integer variables to describe the pump status at the beginning of the scheduling period. This is different from other approaches where binary integer variables for each hour are typically used, which is considered very impractical from an operational perspective. The problem is formulated as a mixed integer nonlinear programming problem, and a new algorithm is developed for its solution. This algorithm is based on the combination of the grid search with the Hooke-Jeeves pattern search method. The performance of the algorithm is evaluated using literature test problems applying the hydraulic simulation model EPANet. © 2012 Elsevier Ltd.
- Description: 2003010583
Hyperbolic smoothing function method for minimax problems
- Authors: Bagirov, Adil , Al Nuaimat, Alia , Sultanova, Nargiz
- Date: 2013
- Type: Text , Journal article
- Relation: Optimization Vol. 62, no. 6 (2013), p. 759-782
- Full Text: false
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- Description: In this article, an approach for solving finite minimax problems is proposed. This approach is based on the use of hyperbolic smoothing functions. In order to apply the hyperbolic smoothing we reformulate the objective function in the minimax problem and study the relationship between the original minimax and reformulated problems. We also study main properties of the hyperbolic smoothing function. Based on these results an algorithm for solving the finite minimax problem is proposed and this algorithm is implemented in general algebraic modelling system. We present preliminary results of numerical experiments with well-known nonsmooth optimization test problems. We also compare the proposed algorithm with the algorithm that uses the exponential smoothing function as well as with the algorithm based on nonlinear programming reformulation of the finite minimax problem. © 2013 Copyright Taylor and Francis Group, LLC.
- Description: 2003011099
Subgradient Method for Nonconvex Nonsmooth Optimization
- Authors: Bagirov, Adil , Jin, L. , Karmitsa, Napsu , Al Nuaimat, A. , Sultanova, Nargiz
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol.157, no.2 (2012), p.416–435
- Full Text: false
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- Description: In this paper, we introduce a new method for solving nonconvex nonsmooth optimization problems. It uses quasisecants, which are subgradients computed in some neighborhood of a point. The proposed method contains simple procedures for finding descent directions and for solving line search subproblems. The convergence of the method is studied and preliminary results of numerical experiments are presented. The comparison of the proposed method with the subgradient and the proximal bundle methods is demonstrated using results of numerical experiments. © 2012 Springer Science+Business Media, LLC.
An incremental clustering algorithm based on hyperbolic smoothing
- Authors: Bagirov, Adil , Ordin, Burak , Ozturk, Gurkan , Xavier, Adilson
- Date: 2015
- Type: Text , Journal article
- Relation: Computational Optimization and Applications Vol. 61, no. 1 (2015), p. 219-241
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex optimization problem. For the most global optimization techniques this problem is challenging even in medium size data sets. In this paper, we propose an approach that allows one to apply local methods of smooth optimization to solve the clustering problems. We apply an incremental approach to generate starting points for cluster centers which enables us to deal with nonconvexity of the problem. The hyperbolic smoothing technique is applied to handle nonsmoothness of the clustering problems and to make it possible application of smooth optimization algorithms to solve them. Results of numerical experiments with eleven real-world data sets and the comparison with state-of-the-art incremental clustering algorithms demonstrate that the smooth optimization algorithms in combination with the incremental approach are powerful alternative to existing clustering algorithms.