Quasidifferentiable Functions Research Articles

Wolfe type duality on quasidifferentiable mathematical programs with vanishing constraints

This article is devoted to the study of duality results for optimization problems with vanishing constraints in nonsmooth case. We formulate Wolfe type dual and establish weak, strong, converse, restricted converse and strict converse duality results for mathematical programs with vanishing constraints involving quasidifferentiable functions. Under the assumption of invex and strictly invex functions with respect to a convex compact set.

Quasiconvex functions: how to separate, if you must!

"Since quasiconvex functions have convex lower level sets it is possible to minimize them by means of separating hyperplanes. An example of such a procedure, well-known for convex functions, is the subgradient method. However, to nd the normal vector of a separating hyperplane is in general not easy for the quasiconvex case. This paper attempts to gain some insight into the computational aspects of determining such a normal vector and the geometry of lower level sets of quasiconvex functions. In order to do so, the directional di erentiability of quasiconvex functions is thoroughly studied. As a consequence of that study, it is shown that an important subset of quasiconvex functions belongs to the class of quasidifferentiable functions. The main emphasis is, however, on computing actual separators. Some important examples are worked out for illustration."

On Continuous Codifferentiability of Quasidifferentiable Functions

On Continuous Codifferentiability of Quasidifferentiable Functions

Quasidifferentiabilities of the expectation functions of random quasidifferentiable functions

This paper explores the quasidifferentiabilities (in the sense of Demyanov and Rubinov) of the expectation functions of random quasidifferentiable functions. Under general assumptions, the expectation function of random quasidifferentiable function is quasidifferentiable and the quasidifferentiation and expectation operators can be interchanged. Furthermore, under some conditions, the expectation functions of random K-differentiable function and random K*-differentiable function are K-differentiable and K*-differentiable, respectively, moreover, whose kernelled and adjoint kernelled quasidifferential equal the expectations of kernelled and adjoint kernelled quasidifferential of random K-differentiable function and K*-differentiable function, respectively.

Условия минимума гладкой функции на границе квазидифференцируемого множества

In this paper, we consider problems of mathematical programming with nonsmoothconstraints of equality type given by quasidifferentiable functions. By using thetechnique of upper convex approximations, developed by B. N. Pshenichy, necessary conditionsof extremum for such problems are established. The Lagrange multipliers signsare specified by virtue of the fact that one can construct whole familers of upper convexapproximations for quasidifferentiable function and thus the minimum points in such extremalproblems are characterized more precisely. Also the simplest problem of calculusof variations with free right hand side is considered, where the left end of the trajectorystarts on the boundary of the convex set. The transversality condition at the left end of thetrajectory is improved provided sertain sufficient conditons hold

О правиле множителей Лагранжа в задачах с ограничениями типа равенства, задаваемые квазидифференцируемыми функциями

In recent years, there has been a steadily growing interest in the study of extremal problems with parameters that do not satisfy the standard smoothness assumptions. This is due to both theoretical needs and important practical applications in economics, technology, physics, and other sciences. Rough objects naturally arise in a several areas of systems analysis, nonlinear mechanics, and control processes. In the theory of extremal problems, the main interest is the behavior of functions in the vicinity of points where a local extremum is attained. The local behavior of nonsmooth functions is described by subgradients, which are analogs of the derivative of differentiable functions. Using the concepts of subdifferential and subgradient F. Clarke proved the Lagrange multiplier rule in mathematical programming problems with constraints of the type of equalities and inequalities defined by locally Lipschitz functions. However, there are subclasses of locally Lipschitz functions, the simplest examples of which show that the necessary conditions for an extremum obtained by F. Clarke are rather crude and do not allow one to discard obviously non-optimal points. Such a subclass of nonsmooth functions is the subspace of quasi-differentiable functions. In this article, using the Eckland variational principle, we obtain the Lagrange multiplier rule in terms of quasi-differentials. It is shown by examples that this condition is stronger than the necessary condition of F. Clarke.

Observability of Linear Time-Varying Systems with Quasiderivative Coefficients

On the basis of quasiderivatives and the upper triangular Hessenberg form, an approach to investigating the observability problem for linear time-varying systems is proposed. The quasiderivatives are defined for some lower triangular matrix, and the simplest rules of the quasidifferentiation are described. Conditions for linear independence of continuous quasidifferentiable functions are established in terms of the Wronski matrix. The necessary and sufficient conditions for an upper triangular Hessenberg form in the orbit of linear time-varying systems are obtained, and the method to find such systems is described. New efficient conditions for various types of observability (complete, differential, uniform) are obtained.

Comparison Between Quasidifferentials and Exhausters

Directional derivatives play one of the major roles in optimization. Optimality conditions can be described in terms of these objects. These conditions, however, are not constructive. To overcome this problem, one has to represent the directional derivative in special forms. Two such forms are quasidifferentials and exhausters proposed by V.F. Demyanov. Quasidifferentials were introduced in 1980s. Optimality conditions in terms of these objects were developed by L.N. Polyakova and V.F. Demyanov. It was described how to find directions of steepest descent and ascent when these conditions are not satisfied. This paved a way for constructing new optimization algorithms. Quasidifferentials allow one to treat a wide class of functions. V.F. Demyanov introduced the notion of exhausters in 2000s to expand the class of functions that can be treated. It should be noted that a great contribution to the emergence of this notion was made by B.N. Pshenichny and A.M. Rubinov. In this work it is shown that exhausters not only allow one to treat a wider class of functions than quasidifferentials (since every quasidifferentiable function has exhausters) but is also preferable even for quasidifferentiable functions when solving nonsmooth optimization problems.

Some relationships among quasidifferential, weak subdifferential and exhausters

In this study, some relationships between quasidifferentiability and weakly subdifferentiability of a function are investigated, and some results on calculating the quasidifferential in terms of weak exhausters are presented. Moreover, under some assumptions weak subdifferentiability of a quasidifferentiable function is proved and the weak subdifferential is obtained by using the quasidifferential. Under some assumptions, it is proved that a positively homogeneous and lower semicontinuous function is quasidifferentiable. It is also shown that directional differentiability and weak subdifferentiability imply quasidifferentiability. Furthermore, a method to evaluate the quasidifferential is given by using reduced weak lower exhausters.

On optimality conditions and duality results in a class of nonconvex quasidifferentiable optimization problems

In the paper, the class of nonconvex nonsmooth optimization problems with the quasidifferentiable functions is considered. Further, a new notion of nonsmooth generalized convexity, namely, the concept of r-invexity with respect to a convex compact set is introduced. Several conditions for quasidifferentiable r-invexity with respect to a convex compact set are given. Furthermore, the sufficient optimality conditions and several Mond–Weir duality results are established for the considered nonconvex quasidifferentiable optimization problem under assumption that the functions constituting it are r-invex with respect to the same function eta and with respect to convex compact sets which are equal to Minkowski sum of their subdifferentials and superdifferentials. It is also illustrated that, for such nonsmooth extremum problems, the Lagrange multipliers may not be constant.

Research on Adjoint Kernelled Quasidifferential

The quasidifferential of a quasidifferentiable function in the sense of Demyanov and Rubinov is not uniquely defined. Xia proposed the notion of the kernelled quasidifferential, which is expected to be a representative for the equivalence class of quasidifferentials. Although the kernelled quasidifferential is known to have good algebraic properties and geometric structure, it is still not very convenient for calculating the kernelled quasidifferentials of−fandminfi∣i∈a finite index set I, wherefandfiare kernelled quasidifferentiable functions. In this paper, the notion of adjoint kernelled quasidifferential, which is well-defined for−fandminfi∣i∈I, is employed as a representative of the equivalence class of quasidifferentials. Some algebraic properties of the adjoint kernelled quasidifferential are given and the existence of the adjoint kernelled quasidifferential is explored by means of the minimal quasidifferential and the Demyanov difference of convex sets. Under some condition, a formula of the adjoint kernelled quasidifferential is presented.

Directed Subdifferentiable Functions and the Directed Subdifferential Without Delta-Convex Structure

We show that the directed subdifferential introduced for differences of convex (delta-convex, DC) functions by Baier and Farkhi can be constructed from the directional derivative without using any information on the delta-convex structure of the function. The new definition extends to a more general class of functions, which includes Lipschitz functions definable on o-minimal structure and quasidifferentiable functions.

Proper exhausters and coexhausters in nonsmooth analysis

There exist many tools to analyze nonsmooth functions. For convex and max-type functions, the notion of subdifferential is used, for quasidifferentiable functions – that of quasidifferential. By means of these tools, one is able to solve, e.g. the following problems: to get an approximation of the increment of a functional, to formulate conditions for an extremum, to find steepest descent and ascent directions and to construct numerical methods. For arbitrary directionally differentiable functions, these problems are solved by employing the notions of upper and lower exhausters and coexhausters, which are generalizations of such notions of nonsmooth analysis as sub- and superdifferentials, quasidifferentials and codifferentials. Exhausters allow one to construct homogeneous approximations of the increment of a functional while coexhausters provide nonhomogeneous approximations. It became possible to formulate conditions for an extremum in terms of exhausters and coexhausters. It turns out that conditions for a minimum are expressed by an upper exhauster, and conditions for a maximum are formulated via a lower one. This is why an upper exhauster is called a proper one for the minimization problem (and adjoint for the maximization problem) while a lower exhauster is called a proper one for the maximization problem (and adjoint for the minimization problem). The conditions obtained provide a simple geometric interpretation and allow one to find steepest descent and ascent directions. In this article, optimization problems are treated by means of proper exhausters and coexhausters.

Adjoint Coexhausters in Nonsmooth Analysis and Extremality Conditions

In the classical (“smooth”) mathematical analysis, a differentiable function is studied by means of the derivative (gradient in the multidimensional space). In the case of nondifferentiable functions, the tools of nonsmooth analysis are to be employed. In convex analysis and minimax theory, the corresponding classes of functions are investigated by means of the subdifferential (it is a convex set in the dual space), quasidifferentiable functions are treated via the notion of quasidifferential (which is a pair of sets). To study an arbitrary directionally differentiable function, the notions of upper and lower exhausters (each of them being a family of convex sets) are used. It turns out that conditions for a minimum are described by an upper exhauster, while conditions for a maximum are stated in terms of a lower exhauster. This is why an upper exhauster is called a proper one for the minimization problem (and an adjoint exhauster for the maximization problem) while a lower exhauster will be referred to as a proper one for the maximization problem (and an adjoint exhauster for the minimization problem). The directional derivatives (and hence, exhausters) provide first-order approximations of the increment of the function under study. These approximations are positively homogeneous as functions of direction. They allow one to formulate optimality conditions, to find steepest ascent and descent directions, to construct numerical methods. However, if, for example, the maximizer of the function is to be found, but one has an upper exhauster (which is not proper for the maximization problem), it is required to use a lower exhauster. Instead, one can try to express conditions for a maximum in terms of upper exhauster (which is an adjoint one for the maximization problem). The first to get such conditions was Roshchina. New optimality conditions in terms of adjoint exhausters were recently obtained by Abbasov. The exhauster mappings are, in general, discontinuous in the Hausdorff metric, therefore, computational problems arise. To overcome these difficulties, the notions of upper and lower coexhausters are used. They provide first-order approximations of the increment of the function which are not positively homogeneous any more. These approximations also allow one to formulate optimality conditions, to find ascent and descent directions (but not the steepest ones), to construct numerical methods possessing good convergence properties. Conditions for a minimum are described in terms of an upper coexhauster (which is, therefore, called a proper coexhauster for the minimization problem) while conditions for a maximum are described in terms of a lower coexhauster (which is called a proper one for the maximization problem). In the present paper, we derive optimality conditions in terms of adjoint coexhausters.

The directed and Rubinov subdifferentials of quasidifferentiable functions, Part II: Calculus

We continue the study of the directed subdifferential for quasidifferentiable functions started in [R. Baier, E. Farkhi, V. Roshchina, The directed and Rubinov subdifferentials of quasidifferentiable functions, Part I: Definition and examples (this journal)]. Calculus rules for the directed subdifferentials of sum, product, quotient, maximum and minimum of quasidifferentiable functions are derived. The relation between the Rubinov subdifferential and the subdifferentials of Clarke, Dini, Michel–Penot, and Mordukhovich is discussed. Important properties implying the claims of Ioffe’s axioms as well as necessary and sufficient optimality conditions for the directed subdifferential are obtained.

The directed and Rubinov subdifferentials of quasidifferentiable functions, Part I: Definition and examples

We extend the definition of the directed subdifferential, originally introduced in [R. Baier, E. Farkhi, The directed subdifferential of DC functions, in: A. Leizarowitz, B.S. Mordukhovich, I. Shafrir, A.J. Zaslavski (Eds.), Nonlinear Analysis and Optimization II: Optimization. A Conference in Celebration of Alex Ioffe’s 70th and Simeon Reich’s 60th Birthdays, June 18–24, 2008, Haifa, Israel, in: AMS Contemp. Math., vol. 513, AMS, Bar-Ilan University, 2010, pp. 27–43], for differences of convex functions (DC) to the wider class of quasidifferentiable functions. Such generalization efficiently captures differential properties of a wide class of functions including amenable and lower/upper- C k functions. While preserving the most important properties of the quasidifferential, such as exact calculus rules, the directed subdifferential lacks the major drawbacks of quasidifferential: non-uniqueness and “inflation in size” of the two convex sets representing the quasidifferential after applying calculus rules. The Rubinov subdifferential is defined as the visualization of the directed subdifferential.

Newton Methods for Quasidifferentiable Equations and Their Convergence

The Newton method and the inexact Newton method for solving quasidifferentiable equations via the quasidifferential are investigated. The notion of Q-semismoothness for a quasidifferentiable function is proposed. The superlinear convergence of the Newton method proposed by Zhang and Xia is proved under the Q-semismooth assumption. An inexact Newton method is developed and its linear convergence is shown.

Representative of Quasidifferentials and Its Formula for a Quasidifferentiable Function

The quasidifferential of a quasidifferentiable function in the sense of Demyanov and Rubinov is not uniquely defined. Xia proposed the notion of the kernelled quasidifferential, which is expected to be a representative for the equivalent class of quasidifferentials. In the 2-dimensional case, the existence of the kernelled quasidifferential was shown. In this paper, the existence of the kernelled quasidifferential in the n-dimensional space (n>2) is proved under the assumption that the Minkowski difference and the Demyanov difference of subdifferential and minus superdifferential coincide. In particular, given a quasidifferential, the kernelled quasidifferential can be formulated. Applications to two classes of generalized separable quasidifferentiable functions are developed.

Identification of point sets by quasidifferentiable functions†

The aim of this article is to realize some ideas of Demyanov (2001) by means of numeric algorithms designed for quasidifferentiable functions.

A gradient-like method for quasidifferentiable optimization

For the minimization of nonsmooth quasidifferentiable functions methods have been defined which make use of a particular structure of the function itself. This usually takes to the knowledge that the quasidifferential is a convex hull of a finite set of gradients and then stopping optimality conditions are sure to be working. In this paper a method for minimizing a general quasidifferentiable function is presented which do not assume that the objective function has any particular structure apart to be quasidifferentiable. A finite set of computed directional derivatives are used to get an approximation of the quasidifferential. The test of optimality condition and the search for a direction of descent are implemented as the solution of convenient subproblems.