CONVEX ANALYSIS and NONLINEAR OPTIMIZATION Theory and Examples
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Nonlinear Optimization Using the Generalized Reduced Gradient Method Revue Française D’Automatique, Informatique, Recherche Opé- Rationnelle
REVUE FRANÇAISE D’AUTOMATIQUE, INFORMATIQUE, RECHERCHE OPÉRATIONNELLE.RECHERCHE OPÉRATIONNELLE LEON S. LASDON RICHARD L. FOX MARGERY W. RATNER Nonlinear optimization using the generalized reduced gradient method Revue française d’automatique, informatique, recherche opé- rationnelle. Recherche opérationnelle, tome 8, no V3 (1974), p. 73-103 <http://www.numdam.org/item?id=RO_1974__8_3_73_0> © AFCET, 1974, tous droits réservés. L’accès aux archives de la revue « Revue française d’automatique, in- formatique, recherche opérationnelle. Recherche opérationnelle » implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/ conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fi- chier doit contenir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ R.A.LR.O. (8* année, novembre 1974, V-3, p. 73 à 104) NONLINEAR OPTIMIZATION USING THE-GENERALIZED REDUCED GRADIENT METHOD (*) by Léon S. LÀSDON, Richard L. Fox and Margery W. RATNER Abstract. — This paper describes the principles and logic o f a System of computer programs for solving nonlinear optimization problems using a Generalized Reduced Gradient Algorithm, The work is based on earlier work of Âbadie (2). Since this paper was written, many changes have been made in the logic, and significant computational expérience has been obtained* We hope to report on this in a later paper. 1. INTRODUCTION Generalized Reduced Gradient methods are algorithms for solving non- linear programs of gênerai structure. This paper discusses the basic principles of GRG, and constructs a spécifie GRG algorithm. The logic of a computer program implementing this algorithm is presented by means of flow charts and discussion. -
Combinatorial Structures in Nonlinear Programming
Combinatorial Structures in Nonlinear Programming Stefan Scholtes¤ April 2002 Abstract Non-smoothness and non-convexity in optimization problems often arise because a combinatorial structure is imposed on smooth or convex data. The combinatorial aspect can be explicit, e.g. through the use of ”max”, ”min”, or ”if” statements in a model, or implicit as in the case of bilevel optimization where the combinatorial structure arises from the possible choices of active constraints in the lower level problem. In analyzing such problems, it is desirable to decouple the combinatorial from the nonlinear aspect and deal with them separately. This paper suggests a problem formulation which explicitly decouples the two aspects. We show that such combinatorial nonlinear programs, despite their inherent non-convexity, allow for a convex first order local optimality condition which is generic and tight. The stationarity condition can be phrased in terms of Lagrange multipliers which allows an extension of the popular sequential quadratic programming (SQP) approach to solve these problems. We show that the favorable local convergence properties of SQP are retained in this setting. The computational effectiveness of the method depends on our ability to solve the subproblems efficiently which, in turn, depends on the representation of the governing combinatorial structure. We illustrate the potential of the approach by applying it to optimization problems with max-min constraints which arise, for example, in robust optimization. 1 Introduction Nonlinear programming is nowadays regarded as a mature field. A combination of important algorithmic developments and increased computing power over the past decades have advanced the field to a stage where the majority of practical prob- lems can be solved efficiently by commercial software. -
Conjugate Convex Functions in Topological Vector Spaces
Matematisk-fysiske Meddelelser udgivet af Det Kongelige Danske Videnskabernes Selskab Bind 34, nr. 2 Mat. Fys. Medd . Dan. Vid. Selsk. 34, no. 2 (1964) CONJUGATE CONVEX FUNCTIONS IN TOPOLOGICAL VECTOR SPACES BY ARNE BRØNDSTE D København 1964 Kommissionær : Ejnar Munksgaard Synopsis Continuing investigations by W. L . JONES (Thesis, Columbia University , 1960), the theory of conjugate convex functions in finite-dimensional Euclidea n spaces, as developed by W. FENCHEL (Canadian J . Math . 1 (1949) and Lecture No- tes, Princeton University, 1953), is generalized to functions in locally convex to- pological vector spaces . PRINTP_ll IN DENMARK BIANCO LUNOS BOGTRYKKERI A-S Introduction The purpose of the present paper is to generalize the theory of conjugat e convex functions in finite-dimensional Euclidean spaces, as initiated b y Z . BIRNBAUM and W. ORLICz [1] and S . MANDELBROJT [8] and developed by W. FENCHEL [3], [4] (cf. also S. KARLIN [6]), to infinite-dimensional spaces . To a certain extent this has been done previously by W . L . JONES in his Thesis [5] . His principal results concerning the conjugates of real function s in topological vector spaces have been included here with some improve- ments and simplified proofs (Section 3). After the present paper had bee n written, the author ' s attention was called to papers by J . J . MOREAU [9], [10] , [11] in which, by a different approach and independently of JONES, result s equivalent to many of those contained in this paper (Sections 3 and 4) are obtained. Section 1 contains a summary, based on [7], of notions and results fro m the theory of topological vector spaces applied in the following . -
Nonlinear Integer Programming ∗
Nonlinear Integer Programming ∗ Raymond Hemmecke, Matthias Koppe,¨ Jon Lee and Robert Weismantel Abstract. Research efforts of the past fifty years have led to a development of linear integer programming as a mature discipline of mathematical optimization. Such a level of maturity has not been reached when one considers nonlinear systems subject to integrality requirements for the variables. This chapter is dedicated to this topic. The primary goal is a study of a simple version of general nonlinear integer problems, where all constraints are still linear. Our focus is on the computational complexity of the problem, which varies significantly with the type of nonlinear objective function in combination with the underlying combinatorial structure. Nu- merous boundary cases of complexity emerge, which sometimes surprisingly lead even to polynomial time algorithms. We also cover recent successful approaches for more general classes of problems. Though no positive theoretical efficiency results are available, nor are they likely to ever be available, these seem to be the currently most successful and interesting approaches for solving practical problems. It is our belief that the study of algorithms motivated by theoretical considera- tions and those motivated by our desire to solve practical instances should and do inform one another. So it is with this viewpoint that we present the subject, and it is in this direction that we hope to spark further research. Raymond Hemmecke Otto-von-Guericke-Universitat¨ Magdeburg, FMA/IMO, Universitatsplatz¨ 2, 39106 Magdeburg, Germany, e-mail: [email protected] Matthias Koppe¨ University of California, Davis, Dept. of Mathematics, One Shields Avenue, Davis, CA, 95616, USA, e-mail: [email protected] Jon Lee IBM T.J. -
An Asymptotical Variational Principle Associated with the Steepest Descent Method for a Convex Function
Journal of Convex Analysis Volume 3 (1996), No.1, 63{70 An Asymptotical Variational Principle Associated with the Steepest Descent Method for a Convex Function B. Lemaire Universit´e Montpellier II, Place E. Bataillon, 34095 Montpellier Cedex 5, France. e-mail:[email protected] Received July 5, 1994 Revised manuscript received January 22, 1996 Dedicated to R. T. Rockafellar on his 60th Birthday The asymptotical limit of the trajectory defined by the continuous steepest descent method for a proper closed convex function f on a Hilbert space is characterized in the set of minimizers of f via an asymp- totical variational principle of Brezis-Ekeland type. The implicit discrete analogue (prox method) is also considered. Keywords : Asymptotical, convex minimization, differential inclusion, prox method, steepest descent, variational principle. 1991 Mathematics Subject Classification: 65K10, 49M10, 90C25. 1. Introduction Let X be a real Hilbert space endowed with inner product :; : and associated norm : , and let f be a proper closed convex function on X. h i k k The paper considers the problem of minimizing f, that is, of finding infX f and some element in the optimal set S := Argmin f, this set assumed being non empty. Letting @f denote the subdifferential operator associated with f, we focus on the contin- uous steepest descent method associated with f, i.e., the differential inclusion du @f(u); t > 0 − dt 2 with initial condition u(0) = u0: This method is known to yield convergence under broad conditions summarized in the following theorem. Let us denote by the real vector space of continuous functions from [0; + [ into X that are absolutely conAtinuous on [δ; + [ for all δ > 0. -
Subderivative-Subdifferential Duality Formula
Subderivative-subdifferential duality formula Marc Lassonde Universit´edes Antilles, BP 150, 97159 Pointe `aPitre, France; and LIMOS, Universit´eBlaise Pascal, 63000 Clermont-Ferrand, France E-mail: [email protected] Abstract. We provide a formula linking the radial subderivative to other subderivatives and subdifferentials for arbitrary extended real-valued lower semicontinuous functions. Keywords: lower semicontinuity, radial subderivative, Dini subderivative, subdifferential. 2010 Mathematics Subject Classification: 49J52, 49K27, 26D10, 26B25. 1 Introduction Tyrrell Rockafellar and Roger Wets [13, p. 298] discussing the duality between subderivatives and subdifferentials write In the presence of regularity, the subgradients and subderivatives of a function f are completely dual to each other. [. ] For functions f that aren’t subdifferentially regular, subderivatives and subgradients can have distinct and independent roles, and some of the duality must be relinquished. Jean-Paul Penot [12, p. 263], in the introduction to the chapter dealing with elementary and viscosity subdifferentials, writes In the present framework, in contrast to the convex objects, the passages from directional derivatives (and tangent cones) to subdifferentials (and normal cones, respectively) are one-way routes, because the first notions are nonconvex, while a dual object exhibits convexity properties. In the chapter concerning Clarke subdifferentials [12, p. 357], he notes In fact, in this theory, a complete primal-dual picture is available: besides a normal cone concept, one has a notion of tangent cone to a set, and besides a subdifferential for a function one has a notion of directional derivative. Moreover, inherent convexity properties ensure a full duality between these notions. [. ]. These facts represent great arXiv:1611.04045v2 [math.OC] 5 Mar 2017 theoretical and practical advantages. -
An Improved Sequential Quadratic Programming Method Based on Positive Constraint Sets, Chemical Engineering Transactions, 81, 157-162 DOI:10.3303/CET2081027
157 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 81, 2020 The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Qiuwang Wang, Min Zeng, Panos Seferlis, Ting Ma, Jiří J. Klemeš Copyright © 2020, AIDIC Servizi S.r.l. DOI: 10.3303/CET2081027 ISBN 978-88-95608-79-2; ISSN 2283-9216 An Improved Sequential Quadratic Programming Method Based on Positive Constraint Sets Li Xiaa,*, Jianyang Linga, Rongshan Bia, Wenying Zhaob, Xiaorong Caob, Shuguang Xianga a State Key Laboratory Base for Eco-Chemical Engineering, College of Chemical Engineering, Qingdao University of Science and Technology, Zhengzhou Road No. 53, Qingdao 266042, China b Chemistry and Chemistry Engineering Faculty,Qilu Normal University, jinan, 250013, Shandong, China [email protected] SQP (sequential quadratic programming) is an effective method to solve nonlinear constraint problems, widely used in chemical process simulation optimization. At present, most optimization methods in general chemical process simulation software have the problems of slow calculation and poor convergence. To solve these problems , an improved SQP optimization method based on positive constraint sets was developed. The new optimization method was used in the general chemical process simulation software named optimization engineers , adopting the traditional Armijo type step rule, L-BFGS (Limited-Memory BFGS) algorithm and rules of positive definite matrix. Compared to the traditional SQP method, the SQP optimization method based on positive constraint sets simplifies the constraint number of corresponding subproblems. L-BFGS algorithm and rules of positive definite matrix simplify the solution of the second derivative matrix, and reduce the amount of storage in the calculation process. -
Appendix a Solving Systems of Nonlinear Equations
Appendix A Solving Systems of Nonlinear Equations Chapter 4 of this book describes and analyzes the power flow problem. In its ac version, this problem is a system of nonlinear equations. This appendix describes the most common method for solving a system of nonlinear equations, namely, the Newton-Raphson method. This is an iterative method that uses initial values for the unknowns and, then, at each iteration, updates these values until no change occurs in two consecutive iterations. For the sake of clarity, we first describe the working of this method for the case of just one nonlinear equation with one unknown. Then, the general case of n nonlinear equations and n unknowns is considered. We also explain how to directly solve systems of nonlinear equations using appropriate software. A.1 Newton-Raphson Algorithm The Newton-Raphson algorithm is described in this section. A.1.1 One Unknown Consider a nonlinear function f .x/ W R ! R. We aim at finding a value of x so that: f .x/ D 0: (A.1) .0/ To do so, we first consider a given value of x, e.g., x . In general, we have that f x.0/ ¤ 0. Thus, it is necessary to find x.0/ so that f x.0/ C x.0/ D 0. © Springer International Publishing AG 2018 271 A.J. Conejo, L. Baringo, Power System Operations, Power Electronics and Power Systems, https://doi.org/10.1007/978-3-319-69407-8 272 A Solving Systems of Nonlinear Equations Using Taylor series, we can express f x.0/ C x.0/ as: Â Ã.0/ 2 Â Ã.0/ df .x/ x.0/ d2f .x/ f x.0/ C x.0/ D f x.0/ C x.0/ C C ::: dx 2 dx2 (A.2) .0/ Considering only the first two terms in Eq. -
A Sequential Quadratic Programming Algorithm with an Additional Equality Constrained Phase
A Sequential Quadratic Programming Algorithm with an Additional Equality Constrained Phase Jos´eLuis Morales∗ Jorge Nocedal † Yuchen Wu† December 28, 2008 Abstract A sequential quadratic programming (SQP) method is presented that aims to over- come some of the drawbacks of contemporary SQP methods. It avoids the difficulties associated with indefinite quadratic programming subproblems by defining this sub- problem to be always convex. The novel feature of the approach is the addition of an equality constrained phase that promotes fast convergence and improves performance in the presence of ill conditioning. This equality constrained phase uses exact second order information and can be implemented using either a direct solve or an iterative method. The paper studies the global and local convergence properties of the new algorithm and presents a set of numerical experiments to illustrate its practical performance. 1 Introduction Sequential quadratic programming (SQP) methods are very effective techniques for solv- ing small, medium-size and certain classes of large-scale nonlinear programming problems. They are often preferable to interior-point methods when a sequence of related problems must be solved (as in branch and bound methods) and more generally, when a good estimate of the solution is available. Some SQP methods employ convex quadratic programming sub- problems for the step computation (typically using quasi-Newton Hessian approximations) while other variants define the Hessian of the SQP model using second derivative informa- tion, which can lead to nonconvex quadratic subproblems; see [27, 1] for surveys on SQP methods. ∗Departamento de Matem´aticas, Instituto Tecnol´ogico Aut´onomode M´exico, M´exico. This author was supported by Asociaci´onMexicana de Cultura AC and CONACyT-NSF grant J110.388/2006. -
Techniques of Variational Analysis
J. M. Borwein and Q. J. Zhu Techniques of Variational Analysis An Introduction October 8, 2004 Springer Berlin Heidelberg NewYork Hong Kong London Milan Paris Tokyo To Tova, Naomi, Rachel and Judith. To Charles and Lilly. And in fond and respectful memory of Simon Fitzpatrick (1953-2004). Preface Variational arguments are classical techniques whose use can be traced back to the early development of the calculus of variations and further. Rooted in the physical principle of least action they have wide applications in diverse ¯elds. The discovery of modern variational principles and nonsmooth analysis further expand the range of applications of these techniques. The motivation to write this book came from a desire to share our pleasure in applying such variational techniques and promoting these powerful tools. Potential readers of this book will be researchers and graduate students who might bene¯t from using variational methods. The only broad prerequisite we anticipate is a working knowledge of un- dergraduate analysis and of the basic principles of functional analysis (e.g., those encountered in a typical introductory functional analysis course). We hope to attract researchers from diverse areas { who may fruitfully use varia- tional techniques { by providing them with a relatively systematical account of the principles of variational analysis. We also hope to give further insight to graduate students whose research already concentrates on variational analysis. Keeping these two di®erent reader groups in mind we arrange the material into relatively independent blocks. We discuss various forms of variational princi- ples early in Chapter 2. We then discuss applications of variational techniques in di®erent areas in Chapters 3{7. -
ADDENDUM the Following Remarks Were Added in Proof (November 1966). Page 67. an Easy Modification of Exercise 4.8.25 Establishes
ADDENDUM The following remarks were added in proof (November 1966). Page 67. An easy modification of exercise 4.8.25 establishes the follow ing result of Wagner [I]: Every simplicial k-"complex" with at most 2 k ~ vertices has a representation in R + 1 such that all the "simplices" are geometric (rectilinear) simplices. Page 93. J. H. Conway (private communication) has established the validity of the conjecture mentioned in the second footnote. Page 126. For d = 2, the theorem of Derry [2] given in exercise 7.3.4 was found earlier by Bilinski [I]. Page 183. M. A. Perles (private communication) recently obtained an affirmative solution to Klee's problem mentioned at the end of section 10.1. Page 204. Regarding the question whether a(~) = 3 implies b(~) ~ 4, it should be noted that if one starts from a topological cell complex ~ with a(~) = 3 it is possible that ~ is not a complex (in our sense) at all (see exercise 11.1.7). On the other hand, G. Wegner pointed out (in a private communication to the author) that the 2-complex ~ discussed in the proof of theorem 11.1 .7 indeed satisfies b(~) = 4. Page 216. Halin's [1] result (theorem 11.3.3) has recently been genera lized by H. A. lung to all complete d-partite graphs. (Halin's result deals with the graph of the d-octahedron, i.e. the d-partite graph in which each class of nodes contains precisely two nodes.) The existence of the numbers n(k) follows from a recent result of Mader [1] ; Mader's result shows that n(k) ~ k.2(~) . -
Proximal Analysis and Boundaries of Closed Sets in Banach Space Part Ii: Applications
Can. J. Math., Vol. XXXIX, No. 2, 1987, pp. 428-472 PROXIMAL ANALYSIS AND BOUNDARIES OF CLOSED SETS IN BANACH SPACE PART II: APPLICATIONS J. M. BORWEIN AND H. M. STROJWAS Introduction. This paper is a direct continuation of the article "Proximal analysis and boundaries of closed sets in Banach space, Part I: Theory", by the same authors. It is devoted to a detailed analysis of applications of the theory presented in the first part and of its limitations. 5. Applications in geometry of normed spaces. Theorem 2.1 has important consequences for geometry of Banach spaces. We start the presentation with a discussion of density and existence of improper points (Definition 1.3) for closed sets in Banach spaces. Our considerations will be based on the "lim inf ' inclusions proven in the first part of our paper. Ti IEOREM 5.1. If C is a closed subset of a Banach space E, then the K-proper points of C are dense in the boundary of C. Proof If x is in the boundary of C, for each r > 0 we may find y £ C with \\y — 3c|| < r. Theorem 2.1 now shows that Kc(xr) ^ E for some xr e C with II* - *,ll ^ 2r. COROLLARY 5.1. ( [2] ) If C is a closed convex subset of a Banach space E, then the support points of C are dense in the boundary of C. Proof. Since for any convex set C and x G C we have Tc(x) = Kc(x) = Pc(x) = P(C - x) = WTc(x) = WKc(x) = WPc(x\ the /^-proper points of C at x, where Rc(x) is any one of the above cones, are exactly the support points.