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M. V. Dolgopolik

Publications and source records attributed to M. V. Dolgopolik.

At least 19 recordsLinked to original sources

A universal convergence theorem for primal-dual penalty and augmented Lagrangian methods

We present a so-called universal convergence theorem for inexact primal-dual penalty and augmented Lagrangian methods that can be applied to a large number of such methods and reduces their convergence analysis to verification of some simple conditions on sequences generated by these methods. If these conditions are verified, then both primal and dual convergence follow directly from the universal convergence theorem. This theorem allows one not only to derive standard convergence theorems for many existing primal-dual penalty and augmented Lagrangian methods in a unified and straightforward manner, but also to strengthen and generalize some of these theorems. In particular, we show how with the use of the universal convergence theorem one can significantly improve some existing results on convergence of a primal-dual rounded weighted $\ell_1$-penalty method, an augmented Lagrangian method for cone constrained optimization, and some other primal-dual methods.

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Convergence analysis of primal-dual augmented Lagrangian methods and duality theory

We develop a unified theory of augmented Lagrangians for nonconvex optimization problems that encompasses both duality theory and convergence analysis of primal-dual augmented Lagrangian methods in the infinite dimensional setting. Our goal is to present many well-known concepts and results related to augmented Lagrangians in a unified manner and bridge a gap between existing convergence analysis of primal-dual augmented Lagrangian methods and abstract duality theory. Within our theory we specifically emphasize the role of various fundamental duality concepts (such as duality gap, optimal dual solutions, global saddle points, etc.) in convergence analysis of augmented Lagrangians methods and underline interconnections between all these concepts and convergence of primal and dual sequences generated by such methods. In particular, we prove that the zero duality gap property is a necessary condition for the boundedness of the primal sequence, while the existence of an optimal dual solution is a necessary condition for the boundedness of the sequences of multipliers and penalty parameters, irrespective of the way in which the multipliers and the penalty parameter are updated. Our theoretical results are applicable to many different augmented Lagrangians for various types of cone constrained optimization problems, including Rockafellar-Wets' augmented Lagrangian, (penalized) exponential/hyperbolic-type augmented Lagrangians, modified barrier functions, etc.

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Exact penalty functions and global saddle points of augmented Lagrangians for well-posed constrained optimization problems

The goal of this article is to study necessary and sufficient conditions for the exactness of penalty functions and the existence of global saddle points of augmented Lagrangians for well-posed (in a suitable sense) constrained optimization problems in infinite dimensional spaces. To this end, we present a new version of extended well-posedness of a constrained optimization problem and analyse how it relates to the more well-known types of well-posedness, such as Tykhonov and Levitin-Polyak well-posedness. This new version of extended well-posedness allows one to extend many existing results on exact penalty functions and global saddle points of augmented Lagrangians from the finite dimensional to the infinite dimensional case. Such extensions provide first verifiable sufficient conditions for the exactness of penalty functions and the existence of global saddle points of augmented Lagrangians in the infinite dimensional case that do not rely on very restrictive and difficult to verify assumptions (nonlocal metric regularity of constraints, existence of nonlocal error bounds, the Palais-Smale condition, abstract properties of the perturbation function, etc.) that are typically used in the literature.

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An acceleration technique for methods for finding the nearest point in a polytope and computing the distance between two polytopes

We present a simple and efficient acceleration technique for an arbitrary method for computing the Euclidean projection of a point onto a convex polytope, defined as the convex hull of a finite number of points, in the case when the number of points in the polytope is much greater than the dimension of the space. The technique consists in applying any given method to a "small" subpolytope of the original polytope and gradually shifting it, till the projection of the given point onto the subpolytope coincides with its projection onto the original polytope. The results of numerical experiments demonstrate the high efficiency of the proposed acceleration technique. In particular, they show that the reduction of computation time increases with an increase of the number of points in the polytope and is proportional to this number for some methods. In the second part of the paper, we also discuss a straightforward extension of the proposed acceleration technique to the case of arbitrary methods for computing the distance between two convex polytopes, defined as the convex hulls of finite sets of points.

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Existence of augmented Lagrange multipliers: reduction to exact penalty functions and localization principle

In this article, we present new general results on existence of augmented Lagrange multipliers. We define a penalty function associated with an augmented Lagrangian, and prove that, under a certain growth assumption on the augmenting function, an augmented Lagrange multiplier exists if and only if this penalty function is exact. We also develop a new general approach to the study of augmented Lagrange multipliers called the localization principle. The localization principle allows one to study the local behaviour of the augmented Lagrangian near globally optimal solutions of the initial optimization problem in order to prove the existence of augmented Lagrange multipliers.

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Augmented Lagrangian Functions for Cone Constrained Optimization: the Existence of Global Saddle Points and Exact Penalty Property

In the article we present a general theory of augmented Lagrangian functions for cone constrained optimization problems that allows one to study almost all known augmented Lagrangians for cone constrained programs within a unified framework. We develop a new general method for proving the existence of global saddle points of augmented Lagrangian functions, called the localization principle. The localization principle unifies, generalizes and sharpens most of the known results on existence of global saddle points, and, in essence, reduces the problem of the existence of saddle points to a local analysis of optimality conditions. With the use of the localization principle we obtain first necessary and sufficient conditions for the existence of a global saddle point of an augmented Lagrangian for cone constrained minimax problems via both second and first order optimality conditions. In the second part of the paper, we present a general approach to the construction of globally exact augmented Lagrangian functions. The general approach developed in this paper allowed us not only to sharpen most of the existing results on globally exact augmented Lagrangians, but also to construct first globally exact augmented Lagrangian functions for equality constrained optimization problems, for nonlinear second order cone programs and for nonlinear semidefinite programs. These globally exact augmented Lagrangians can be utilized in order to design new superlinearly (or even quadratically) convergent optimization methods for cone constrained optimization problems.

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Exact augmented Lagrangians for constrained optimization problems in Hilbert spaces II: Applications

This two-part study is devoted to the analysis of the so-called exact augmented Lagrangians, introduced by Di Pillo and Grippo for finite dimensional optimization problems, in the case of optimization problems in Hilbert spaces. In the second part of our study we present applications of the general theory of exact augmented Lagrangians to several constrained variational problems and optimal control problems, including variational problems with additional constraints at the boundary, isoperimetric problems, problems with nonholonomic equality constraints (PDE constraints), and optimal control problems for linear evolution equations. We provide sufficient conditions for augmented Lagrangians for these problems to be globally/completely exact, that is, conditions under which a constrained variational problem/optimal control problem becomes equivalent to the problem of unconstrained minimization of the corresponding exact augmented Lagrangian in primal and dual variables simultaneously.

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Hypodifferentials of nonsmooth convex functions and their applications to nonsmooth convex optimization

A hypodifferential is a compact family of affine mappings that defines a local max-type approximation of a nonsmooth convex function. We present a general theory of hypodifferentials of nonsmooth convex functions defined on a Banach space. In particular, we provide complete characterizations of hypodifferentiability and hypodifferentials of nonsmooth convex functions, derive calculus rules for hypodifferentials, and study the Lipschitz continuity/Lipschitz approximation property of hypodifferentials that can be viewed as a natural extension of the Lipschitz continuity of the gradient to the general nonsmooth setting. As an application of our theoretical results, we study the rate of convergence of several versions of the method of hypodifferential descent for nonsmooth convex optimization and present an accelerated version of this method having the faster rater of convergence $\mathcal{O}(1/k^2)$.

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A note on the generalised Hessian of the least squares associated with systems of linear inequalities

The goal of this note is to point out an erroneous formula for the generalised Hessian of the least squares associated with a system of linear inequalities, that was given in the paper "A finite Newton method for classification" by O.L. Mangasarian (Optim. Methods Softw. 17: 913--929, 2002) and reproduced multiple times in other publications. We also provide sufficient contiditions for the validity of Mangasarian's formula and show that Slater's condition guarantees that some particular elements from the set defined by Mangasarian belong to the generalised Hessian of the corresponding function.

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Codifferentials and Quasidifferentials of the Expectation of Nonsmooth Random Integrands and Two-Stage Stochastic Programming

This work is devoted to an analysis of exact penalty functions and optimality conditions for nonsmooth two-stage stochastic programming problems. To this end, we first study the co-/quasi-differentiability of the expectation of nonsmooth random integrands and obtain explicit formulae for its co- and quasidifferential under some natural assumptions on the integrand. Then we analyse exact penalty functions for a variational reformulation of two-stage stochastic programming problems and obtain sufficient conditions for the global exactness of these functions with two different penalty terms. In the end of the paper, we combine our results on the co-/quasi-differentiability of the expectation of nonsmooth random integrands and exact penalty functions to derive optimality conditions for nonsmooth two-stage stochastic programming problems in terms of codifferentials.

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Subdifferentials of convex matrix-valued functions

Subdifferentials (in the sense of convex analysis) of matrix-valued functions defined on $\mathbb{R}^d$ that are convex with respect to the Löwner partial order can have a complicated structure and might be very difficult to compute even in simple cases. The aim of this paper is to study subdifferential calculus for such functions and properties of their subdifferentials. We show that many standard results from convex analysis no longer hold true in the matrix-valued case. For example, in this case the subdifferential of the sum is not equal to the sum of subdifferentials, the Clarke subdifferential is not equal to the subdifferential in the sense of convex analysis, etc. Nonetheless, it is possible to provide simple rules for computing nonempty subsets of subdifferentials (in particular, individual subgradients) of convex matrix-valued functions in the general case and to completely describe subdifferentials of such functions defined on the real line. As a by-product of our analysis, we derive some interesting properties of convex matrix-valued functions, e.g. we show that if such function is nonsmooth, then its diagonal elements must be nonsmooth as well.

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Nonlocal error bounds for piecewise affine functions

The paper is devoted to a detailed analysis of nonlocal error bounds for nonconvex piecewise affine functions. We both improve some existing results on error bounds for such functions and present completely new necessary and/or sufficient conditions for a piecewise affine function to have an error bound on various types of bounded and unbounded sets. In particular, we show that any piecewise affine function has an error bound on an arbitrary bounded set and provide several types of easily verifiable sufficient conditions for such functions to have an error bound on unbounded sets. We also present general necessary and sufficient conditions for a piecewise affine function to have an error bound on a finite union of polyhedral sets (in particular, to have a global error bound), whose derivation reveals a structure of sublevel sets and recession functions of piecewise affine functions.

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DC Semidefinite Programming and Cone Constrained DC Optimization: Theory and Local Search Methods

In this paper, we study possible extensions of the main ideas and methods of constrained DC optimization to the case of nonlinear semidefinite programming problems and more general nonlinear and nonsmooth cone constrained optimization problems. In the first part of the paper, we analyse two different approaches to the definition of DC matrix-valued functions (namely, order-theoretic and componentwise), study some properties of convex and DC matrix-valued mappings and demonstrate how to compute DC decompositions of some nonlinear semidefinite constraints appearing in applications. We also compute a DC decomposition of the maximal eigenvalue of a DC matrix-valued function. This DC decomposition can be used to reformulate DC semidefinite constraints as DC inequality constrains. Finally, we study local optimality conditions for general cone constrained DC optimization problems. The second part of the paper is devoted to a detailed convergence analysis of two extensions of the well-known DCA method for solving DC (Difference of Convex functions) optimization problems to the case of general cone constrained DC optimization problems. We study the global convergence of the DCA for cone constrained problems and present a comprehensive analysis of a version of the DCA utilizing exact penalty functions. In particular, we study the exactness property of the penalized convex subproblems and provide two types of sufficient conditions for the convergence of the exact penalty method to a feasible and critical point of a cone constrained DC optimization problem from an infeasible starting point. In the numerical section of this work, the exact penalty DCA is applied to the problem of computing compressed modes for variational problems and the sphere packing problem on Grassmannian.

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Exact augmented Lagrangians for constrained optimization problems in Hilbert spaces I: Theory

In this two-part study, we develop a general theory of the so-called exact augmented Lagrangians for constrained optimization problems in Hilbert spaces. In contrast to traditional nonsmooth exact penalty functions, these augmented Lagrangians are continuously differentiable for smooth problems and do not suffer from the Maratos effect, which makes them especially appealing for applications in numerical optimization. Our aim is to present a detailed study of various theoretical properties of exact augmented Lagrangians and discuss several applications of these functions to constrained variational problems, problems with PDE constraints, and optimal control problems. The first paper is devoted to a theoretical analysis of an exact augmented Lagrangian for optimization problems in Hilbert spaces. We obtain several useful estimates of this augmented Lagrangian and its gradient, and present several types of sufficient conditions for KKT-points of a constrained problem corresponding to locally/globally optimal solutions to be local/global minimisers of the exact augmented Lagrangian.

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The method of codifferential descent for convex and global piecewise affine optimization

The class of nonsmooth codifferentiable functions was introduced by professor V.F.~Demyanov in the late 1980s. He also proposed a method for minimizing these functions called the method of codifferential descent (MCD). However, until now almost no theoretical results on the performance of this method on particular classes of nonsmooth optimization problems were known. In the first part of the paper, we study the performance of the method of codifferential descent on a class of nonsmooth convex functions satisfying some regularity assumptions, which in the smooth case are reduced to the Lipschitz continuity of the gradient. We prove that in this case the MCD has the iteration complexity bound $\mathcal{O}(1 / \varepsilon)$. In the second part of the paper we obtain new global optimality conditions for piecewise affine functions in terms of codifferentials. With the use of these conditions we propose a modification of the MCD for minimizing piecewise affine functions (called the method of global codifferential descent) that does not use line search, and discards those "pieces" of the objective functions that are no longer useful for the optimization process. Then we prove that the MCD as well as its modification proposed in the article find a point of global minimum of a nonconvex piecewise affine function in a finite number of steps.

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An adaptive exact penalty method for nonsmooth optimal control problems with nonsmooth nonconvex state and control constraints

A class of exact penalty-type local search methods for optimal control problems with nonsmooth cost functional, nonsmooth (but continuous) dynamics, and nonsmooth state and control constraints is presented, in which the the penalty parameter and several line search parameters are adaptively adjusted during the optimisation process. This class of methods is applicable to problems having a known DC (Difference-of-Convex functions) structure and in its core is based on the classical DCA method, combined with the steering exact penalty rules for updating the penalty parameter and an adaptive nonmonotone line search procedure. Under the assumption that all auxiliary subproblems are solved only approximately (that is, with finite precision), we prove the correctness of the proposed family of methods and present its detailed convergence analysis. The performance of several different versions of the method is illustrated by means of a numerical example, in which the methods are applied to a semi-academic optimal control problem with a nonsmooth nonconvex state constraint.

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Steering exact penalty DCA for nonsmooth DC optimization problems with equality and inequality constraints

We propose and study a version of the DCA (Difference-of-Convex functions Algorithm) using the $\ell_1$ penalty function for solving nonsmooth DC optimization problems with nonsmooth DC equality and inequality constraints. The method employs an adaptive penalty updating strategy to improve its performance. This strategy is based on the so-called steering exact penalty methodology and relies on solving some auxiliary convex subproblems to determine a suitable value of the penalty parameter. We present a detailed convergence analysis of the method and illustrate its practical performance by applying the method to two nonsmooth discrete optimal control problem.

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Exact penalty functions with multidimensional penalty parameter and adaptive penalty updates

We present a general theory of exact penalty functions with vectorial (multidimensional) penalty parameter for optimization problems in infinite dimensional spaces. In comparison with the scalar case, the use of vectorial penalty parameters provides much more flexibility, allows one to adaptively and independently take into account the violation of each constraint during an optimization process, and often leads to a better overall performance of an optimization method using an exact penalty function. We obtain sufficient conditions for the local and global exactness of penalty functions with vectorial penalty parameters and study convergence of global exact penalty methods with several different penalty updating strategies. In particular, we present a new algorithmic approach to an analysis of the global exactness of penalty functions, which contains a novel characterisation of the global exactness property in terms of behaviour of sequences generated by certain optimization methods.

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