Abstract convex approximations of nonsmooth functions

In the article we use abstract convexity theory in order to unify and generalize many different concepts of nonsmooth analysis. We introduce the concepts of abstract codifferentiability, abstract quasidifferentiability and abstract convex (concave) approximations of a nonsmooth function mapping a topological vector space to an order complete topological vector lattice. We study basic properties of these notions, construct elaborate calculus of abstract codifferentiable functions and discuss continuity of abstract codifferential. We demonstrate that many classical concepts of nonsmooth analysis, such as subdifferentiability and quasidifferentiability, are particular cases of the concepts of abstract codifferentiability and abstract quasidifferentiability. We also show that abstract convex and abstract concave approximations are a very convenient tool for the study of nonsmooth extremum problems. We use these approximations in order to obtain various necessary optimality conditions for nonsmooth nonconvex optimization problems with the abstract codifferentiable or abstract quasidifferentiable objective function and constraints. Then, we demonstrate how these conditions can be transformed into simpler and more constructive conditions in some particular cases.     

α-次积分C-半群与抽象柯西问题

本文引入一般的α-次积分C-半群和mildα-次积分C-存在族概念,并讨论它们与抽象柯西问题的联系．     

Generalized solution principles and out-ranking relations in multi-criteria decision-making

In this paper the abstract notion of a solution principle for multi-criteria decision-making problems is introduced as a set-valued mapping which associates to each decision problem (several) sets of potential solutions. Some modeling conditions are imposed on solution principles which relate them with binary (preference or outranking) relations; this relationship is studied, particularly, the concepts of kernel, quasi-kernel, subsolution and supercore are discussed in terms of solution principles.     

Reducing abstraction: The case of constructing an operation table for a group

This paper describes students' mental processes while constructing an operation table for a group. More specifically, undergraduate students' approaches are analyzed as the students fill in an operation table for four elements—a, b, c, and d—in such a way that it represents a group of order four. The data are analyzed from the perspective of reducing abstraction, which aims to explain students' conceptions of abstract algebra concepts. From this perspective, most students' responses and conceptions can be attributed to their tendency to work on a lower level of abstraction than the level on which concepts are introduced in class.     

Topological structures of complex belief systems

The concepts of substantive beliefs and derived beliefs are defined, a set of substantive beliefs S like open set and the neighborhood of an element substantive belief. A semantic operation of conjunction is defined with a structure of an Abelian group. Mathematical structures exist such as poset beliefs and join‐semilattttice beliefs. A metric space of beliefs and the distance of belief depending on the believer are defined. The concepts of closed and opened ball are defined. S′ is defined as subgroup of the metric space of beliefs Σ and S′ is a totally limited set. The term s is defined (substantive belief) in terms of closing of S′. It is deduced that Σ is paracompact due to Stone's Theorem. The pseudometric space of beliefs is defined to show how the metric of the nonbelieving subject has a topological space like a nonmaterial abstract ideal space formed in the mind of the believing subject, fulfilling the conditions of Kuratowski axioms of closure. To establish patterns of materialization of beliefs we are going to consider that these have defined mathematical structures. This will allow us to understand better cultural processes of text, architecture, norms, and education that are forms or the materialization of an ideology. This materialization is the conversion by means of certain mathematical correspondences, of an abstract set whose elements are beliefs or ideas, in an impure set whose elements are material or energetic. Text is a materialization of ideology. © 2013 Wiley Periodicals, Inc. Complexity 19: 46–62, 2013     

The impact of sampling methods on bias and variance in stochastic linear programs

Stochastic linear programs can be solved approximately by drawing a subset of all possible random scenarios and solving the problem based on this subset, an approach known as sample average approximation (SAA). The value of the objective function at the optimal solution obtained via SAA provides an estimate of the true optimal objective function value. This estimator is known to be optimistically biased; the expected optimal objective function value for the sampled problem is lower (for minimization problems) than the optimal objective function value for the true problem. We investigate how two alternative sampling methods, antithetic variates (AV) and Latin Hypercube (LH) sampling, affect both the bias and variance, and thus the mean squared error (MSE), of this estimator. For a simple example, we analytically express the reductions in bias and variance obtained by these two alternative sampling methods. For eight test problems from the literature, we computationally investigate the impact of these sampling methods on bias and variance. We find that both sampling methods are effective at reducing mean squared error, with Latin Hypercube sampling outperforming antithetic variates. For our analytic example and the eight test problems we derive or estimate the condition number as defined in Shapiro et al. (Math. Program. 94:1–19, 2002). We find that for ill-conditioned problems, bias plays a larger role in MSE, and AV and LH sampling methods are more likely to reduce bias.     

Second-order conditions and constraint qualifications in stability and sensitivity analysis of solutions to optimization problems in Hilbert spaces

The concepts of the strong second-order sufficient optimality condition and of the linear independence of gradients of active constraints play a crucial role in stability and sensitivity analysis of solutions to finite-dimensional mathematical programming problems. In this paper an attempt is made to use these concepts in stability and sensitivity analysis of solutions to cone-constrained optimization problems in Hilbert spaces. The abstract results are applied to optimal control problems for affine systems subject to state-space constraints.This research was completed when the author was a visitor at the Institut für Angewandte Mathematik und Statistik of the University of Würzburg, and it was partially supported by the Deutsche Forschungsgemeinschaft (DFG); SPP Anwendungsbezogene Optimierung und Steuergung.     

Sharp Extensions for Convoluted Solutions of Abstract Cauchy Problems

In this paper we give sharp extension results for convoluted solutions of abstract Cauchy problems in Banach spaces. The main technique is the use of the algebraic structure (for the usual convolution product *) of these solutions which are defined by a version of the Duhamel formula. We define algebra homomorphisms from a new class of test-functions and apply our results to concrete operators. Finally, we introduce the notion of k-distribution semigroups to extend previous concepts of distribution semigroups.     

MULTIGRID METHODS FOR THE GENERALIZED STOKES EQUATIONS BASED ON MIXED FINITE ELEMENT METHODS

1. IntroductionIn this paper, we consider the fOllowing generalized stationary Stokes equations:where fl is a bounded convex domain in R', u represents the velocity of fluid, p its pressure; Fand G are external fOrce and source terms. Note that the source…     

A Categorical View to Structural Complexity

In the present work we are interested in to provide a universal language for supporting formalisms to specify the approximation hierarchy system for an abstract NP‐hard optimization problem. This work grew from the idea of providing a categorical view of structural complexity to optimization problems. The direction is aimed towards actually exploring the connections among the structural complexity aspects and categorical concepts, which may be viewed in a high‐level, in a structuralistic sense. After introducing the optimization problems categories OPTS and OPT, as well as related questions, a formal system modelling the approximation hierarchy of a given optimization problem is provided, based on categorical shape theory. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Reconstructing initial data using observers: error analysis of the semi-discrete and fully discrete approximations

A new iterative algorithm for solving initial data inverse problems from partial observations has been recently proposed in Ramdani et al. (Automatica 46(10), 1616–1625, 2010). Based on the concept of observers (also called Luenberger observers), this algorithm covers a large class of abstract evolution PDE’s. In this paper, we are concerned with the convergence analysis of this algorithm. More precisely, we provide a complete numerical analysis for semi-discrete (in space) and fully discrete approximations derived using finite elements in space and an implicit Euler method in time. The analysis is carried out for abstract Schr?dinger and wave conservative systems with bounded observation (locally distributed).     

关于一般化凸乘积空间上的极大元和平衡点

利用一般化凸乘积空间上的Fan-Browder型不动点定理给出了新的极大元存在定理,然后定义了两个概念：“类Uθ”和“类V”,并讨论了在抽象经济中平衡点的存在性问题．文中所得结论改进和推广了文献中的相应结果．     

Necessary and sufficient conditions for weak efficiency in non-smooth vectorial optimization problems

By using the concepts of local cone approximations and K-directional derivatives, we obtain necessary conditions of optimality for the weak efficiency of the vectorial optimization problems with the inequalities and abstract constraints. We introduce the notion of stationary point (weak and strong) and we prove that, under suitable hypotheses of K-invexity, the stationary points are weakly efficient solutions (global).     

About the correspondences between dual heyting arrow lattices of a distributive lattice and its sublattices for relational biologic systems

Heyting and dual Heyting arrow operations relating non-comparable elements of finite relatively pseudo-complemented lattices being pseudo-Boolean algebras (L) gave place to new structures named Heyting arrow (L_F) and dual Heyting arrow lattices ( _F) (though sometimes they are only posets). They were used for analyzing qualitative relations in biological systems by means of isomorphisms relating the lattice elements with energy states identified through abstract relational concepts describing the system being represented.

This paper considers the problem of connecting the poset with the posets (κ) corresponding to the epimorphic images L_κ of a pseudo-Boolean lattice L.     

Antithetic variate methods for simulations of processes with peaks and troughs

To save costs in computer simulations, the well-known method of antithetic variates is often suggested for reducing the variability of estimated quantities. The method can be a dangerous one to use however, as, if incorrectly applied, it can be useless or worse than useless. Recent studies have shown how the method can be used successfully, but these have concentrated mainly on steady state systems and the quantities of interest in such processes.In this paper we consider problems where maxima and minima are of interest and suggest simple antithetic strategies which can be applied in such situations. The methods have the advantage of not being particularly problem dependent so that they can be applied in a fairly automatic manner by the call to set routines at certain points in a simulation.The methods are illustrated by two practical case studies. One involves the forecasting of peak gas demand and the other involves the study of the drought and flooding characteristics of a river system. In both examples the computing costs are high. It is shown that the suggested methods are easily applied and lead to significant savings in cost.     

Density, Overcompleteness, and Localization of Frames. II. Gabor Systems

This work develops a quantitative framework for describing the overcompleteness of a large class of frames. A previous article introduced notions of localization and approximation between two frames F = {f_i}_i∈I and E = {e_j}_j∈G (G a discrete abelian group), relating the decay of the expansion of the elements of F in terms of the elements of E via a map a : I → G. This article shows that those abstract results yield an array of new implications for irregular Gabor frames. Additionally, various Nyquist density results for Gabor frames are recovered as special cases, and in the process both their meaning and implications are clarified. New results are obtained on the excess and overcompleteness of Gabor frames, on the relationship between frame bounds and density, and on the structure of the dual frame of an irregular Gabor frame. More generally, these results apply both to Gabor frames and to systems of Gabor molecules, whose elements share only a common envelope of concentration in the time-frequency plane. The notions of localization and related approximation properties are a spectrum of ideas that quantify the degree to which elements of one frame can be approximated by elements of another frame. In this article, a comprehensive examination of the interrelations among these localization and approximation concepts is made, with most implications shown to be sharp.     

Balancing domain decomposition for problems with large jumps in coefficients

The Balancing Domain Decomposition algorithm uses in each iteration solution of local problems on the subdomains coupled with a coarse problem that is used to propagate the error globally and to guarantee that the possibly singular local problems are consistent. The abstract theory introduced recently by the first-named author is used to develop condition number bounds for conforming linear elements in two and three dimensions. The bounds are independent of arbitrary coefficient jumps between subdomains and of the number of subdomains, and grow only as the squared logarithm of the mesh size . Computational experiments for two- and three-dimensional problems confirm the theory.

     

Algebra homomorphisms defined via convoluted semigroups and cosine functions

Transform methods are used to establish algebra homomorphisms related to convoluted semigroups and convoluted cosine functions. Such families are now basic in the study of the abstract Cauchy problem. The framework they provide is flexible enough to encompass most of the concepts used up to now to treat Cauchy problems of the first- and second-order in general Banach spaces. Starting with the study of the classical Laplace convolution and a cosine convolution, along with associated dual transforms, natural algebra homomorphisms are introduced which capture the convoluted semigroup and cosine function properties. These correspond to extensions of the Cauchy functional equation for semigroups and the abstract d'Alembert equation for the case of cosine operator functions. The algebra homomorphisms obtained provide a way to prove Hille-Yosida type generation theorems for the operator families under consideration.     

Elliptic equations in hilbert space and associated spectral problems

Problems are formulated for abstract higher-order elliptic equations on the semiaxis and on a finite interval and general theorems for the Fredholm solvability and exact solvability of these equations given emission conditions to infinity are proved. A classification of the real spectrum of the pencil associated with the equation is presented, and possible rules for rigorous selection of the segment of its eigenelements and associated elements formulated. Completeness, minimality, and the basis property of the fundamental solutions of the equation in the solution space, along with the properties of the derivative chains of the eigenelements and associated elements of the pencil that correspond to problems on the semiaxis and on a finite interval are studied.Translated from Trudy Seminara imeni I. G. Petrovskogo, No. 14, pp. 140–224, 1989.     

Abstract subdifferentials and some characterizations of optimal solutions

We introduce and study the subdifferential of a function at a point, with respect to a primal-dual pair of optimization problems, which encompasses, as particular cases, several known concepts of subdifferential. We give a characterization of optimal solutions of the primal problem, in terms of abstract Lagrangians, and a simultaneous characterization of optimal solutions and strong duality, with the aid of abstract subdifferentials. We give some applications to unperturbational Lagrangian duality and unperturbational surrogate duality.We wish to thank H. J. Greenberg for discussions and valuable remarks on the subject of this paper, made during his visit in Bucharest, in May 1985, within the framework of the Cooperative Exchange Agreement between the National Academy of Sciences of the USA and the Romanian Academy of Sciences.