Book review of Israel Gohberg,Seymour Goldberg,Marinus A. Kaashoek,Basic Classes of Linear Operators,Birkhäuser Verlag,Basel–Boston–Berlin 2003, 423 p. ISBN 3-7643-6930-2

In projective space three notions of convexity (weak convexity, strong convexity, p-convexity) are regarded systematically. Since these notions are defined only by incidence relations, there can be introduced dual notions. We consider relations.between the introduced notions and the most essential properties of convex sets. To all assertions. can be formulated dual assertions, too. The most important theorems given by Fenchel can be generalised. The property of a point set (a set of hyperplanes) to be strongly convex or p-convex, respectively, is invariant with respect to correlations.     

Duality theorem for B‐valued martingale Orlicz–Hardy spaces associated with concave functions

The dual space of B ‐valued martingale Orlicz–Hardy space with a concave function Φ, which is associated with the conditional p‐variation of B ‐valued martingale, is characterized. To obtain the results, a new type of Campanato spaces for B ‐valued martingales is introduced and the classical technique of atomic decompositions is improved. Some results obtained here are connected closely with the p‐uniform smoothness and q‐uniform convexity of the underlying Banach space.     

Risk measures on the space of infinite sequences

Axiomatically based risk measures have been the object of numerous studies and generalizations in recent years. In the literature we find two main schools: coherent risk measures (Artzner, Coherent Measures of Risk. Risk Management: Value at Risk and Beyond, 1998) and insurance risk measures (Wang, Insur Math Econ 21:173–183, 1997). In this note, we set to study yet another extension motivated by a third axiomatically based risk measure that has been recently introduced. In Heyde et al. (Working Paper, Columbia University, 2007), the concept of natural risk statistic is discussed as a data-based risk measure, i.e. as an axiomatic risk measure defined in the space \mathbb Rⁿ{\mathbb R^n} . One drawback of these kind of risk measures is their dependence on the space dimension n. In order to circumvent this issue, we propose a way to define a family {ρ _n } _n=1,2,... of natural risk statistics whose members are defined on \mathbbRⁿ{\mathbb{R}^n} and related in an appropriate way. This construction requires the generalization of natural risk statistics to the space of infinite sequences l ^∞.     

Asymptotic structure, -estimates of sequences, and compactness of multilinear mappings

We relate the moduli of asymptotic uniform smoothness and convexity of a Banach space with the existence of upper and lower ℓ_p-estimates of sequences in the space. To this end, we introduce two properties which are related to the (m_p)-property defined by Kalton and Werner. In this way we obtain a connection between the moduli of asymptotic uniform smoothness and convexity, and compactness or weak-sequential continuity of multilinear mappings. Finally, we give some applications to the existence of analytic and asymptotically flat norms on a Banach space.     

Infinite Dimensional Geometric Properties of Real Interpolation Spaces

Estimates for the moduli of noncompact convexity of l_p-sums and real interpolation spaces for finite families of spaces are given. It is proved that such an interpolation preserves nearly uniform convexity and property (β).     

Algorithmic Consistency in Computational Inelasticity – a Conceptual Completion

This paper communicates a new algorithmic concept, how higher-order Runge-Kutta (RK) methods for time integration of viscoelastic constitutive laws can be introduced into nonlinear finite element methods in order (i) to obtain the full nominal order p in time integration, (ii) to ensure that global equilibrium is only required at the end of time intervals Δt but not in the interior at RK-stages, and (iii) to obtain –based on (i) and (ii)– a considerable speed-up compared with Backward-Euler. The condition to realize (i)–(iii) is, that the approximation of total strain in time must be of the same order as the time-integration method, which is a completion of the concept of algorithmic consistency in computational inelasticity. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Sharp uniform convexity and smoothness inequalities for trace norms

Summary We prove several sharp inequalities specifying the uniform convexity and uniform smoothness properties of the Schatten trace idealsC _p , which are the analogs of the Lebesgue spacesL _p in non-commutative integration. The inequalities are all precise analogs of results which had been known inL _p , but were only known inC _p for special values ofp. In the course of our treatment of uniform convexity and smoothness inequalities forC _p we obtain new and simple proofs of the known inequalities forL _p .Oblatum 7-VII-1993Work partially supported by US National Science Foundation grant DMS 88-07243Work partially supported by US National Science Foundation grant DMS 92-07703Work partially supported by US National Science Foundation grant PHY90-19433 A02     

Low order-value approach for solving VaR-constrained optimization problems

In Low Order-Value Optimization (LOVO) problems the sum of the r smallest values of a finite sequence of q functions is involved as the objective to be minimized or as a constraint. The latter case is considered in the present paper. Portfolio optimization problems with a constraint on the admissible Value at Risk (VaR) can be modeled in terms of a LOVO problem with constraints given by Low order-value functions. Different algorithms for practical solution of this problem will be presented. Using these techniques, portfolio optimization problems with transaction costs will be solved.     

Simulation and evaluation of the distribution of interest rate risk

We study methods to simulate term structures in order to measure interest rate risk more accurately. We use principal component analysis of term structure innovations to identify risk factors and we model their univariate distribution using GARCH-models with Student’s t-distributions in order to handle heteroscedasticity and fat tails. We find that the Student’s t-copula is most suitable to model co-dependence of these univariate risk factors. We aim to develop a model that provides low ex-ante risk measures, while having accurate representations of the ex-post realized risk. By utilizing a more accurate term structure estimation method, our proposed model is less sensitive to measurement noise compared to traditional models. We perform an out-of-sample test for the U.S. market between 2002 and 2017 by valuing a portfolio consisting of interest rate derivatives. We find that ex-ante Value at Risk measurements can be substantially reduced for all confidence levels above 95%, compared to the traditional models. We find that that the realized portfolio tail losses accurately conform to the ex-ante measurement for daily returns, while traditional methods overestimate, or in some cases even underestimate the risk ex-post. Due to noise inherent in the term structure measurements, we find that all models overestimate the risk for 10-day and quarterly returns, but that our proposed model provides the by far lowest Value at Risk measures.

     

Generalization of certain subclasses of multivalent functions with negative coefficients defined by using a differential operator

Making use of a differential operator, we introduce and study a certain class SC_n(j,p,q,α,λ) of p-valently analytic functions with negative coefficients. In this paper, we obtain numerous sharp results including (for example ) coefficient estimates, distortion theorem, radii of close-to convexity, starlikeness and convexity and modified Hadamard products of functions belonging to the class SC_n(j,p,q,α,λ). Finally, several applications investigate an integral operator, and certain fractional calculus operators are also considered.     

A microscopic convexity theorem of level sets for solutions to elliptic equations

We study the microscopic level-set convexity theorem for elliptic equation Lu?=?f(x, u, Du), which generalize Korevaars?? result in (Korevaar, Commun Part Diff Eq 15(4):541?C556, 1990) by using different expression for the elementary symmetric functions of the principal curvatures of the level surface. We find out that the structure conditions on equation are as same as conditions in macroscopic level-set convexity results (see e.g. (Colesanti and Salani, Math Nachr 258:3?C15, 2003; Greco, Bound Value Prob 1?C15, 2006)). In a forthcoming paper, we use the same techniques to deal with Hessian type equations.     

Properties and calculation of multivariate risk measures: MVaR and MCVaR

A recent paper by Prékopa (Ann. Oper. Res. 193(1):49–69, 2012) presented results in connection with Multivariate Value-at-Risk (MVaR) that has been known for some time under the name of p-quantile or p-Level Efficient Point (pLEP) and introduced a new multivariate risk measure, called Multivariate Conditional Value-at-Risk (MCVaR). The purpose of this paper is to further develop the theory and methodology of MVaR and MCVaR. This includes new methods to numerically calculate MCVaR, for both continuous and discrete distributions. Numerical examples with recent financial market data are presented.     

L p Estimates for Maxwell's Equations with Source Term

The goal of this paper is to establish interior and global L ^p -type estimates for the solutions of Maxwell's equations with source term in a domain filled with two different materials separated by a ² interface. The usual elliptic estimates cannot be applied directly, due to the singularity of the dielectric permittivity. A special curl-div decomposition is introduced for the electric field to reduce the problem to an elliptic equation in divergence form with jump coefficients. The potential analysis and the jump condition lead to the interior L ^p estimates which are superior to the straightforward Nash-Moser estimates. The reduction procedure is expected to be useful for future numerical simulation. Because of the natural physical requirements, the boundary condition is nonlocal and involves a first order pseudo-differential operator, the boundary estimate is established by delicate new maximum principles and Riesz convexity arguments. These estimates are then employed to solve a nonlinear optics problem that arises in the modeling of surface enhanced second-harmonic generation of nonlinear diffractive optics in periodic structures (gratings).     

Computable examples of the maximal Lyapunov exponent

Summary Some new examples are given of sequences of matrix valued random variables for which it is possible to compute the maximal Lyapunov exponent. The examples are constructed by using a sequence of stopping times to group the original sequence into commuting blocks. If the original sequence is the outcome of independent Bernoulli trials with success probability p, then the maximal Lyapunov exponent may be expressed in terms of power series in p, with explicit formulae for the coefficients. The convexity of the maximal Lyapunov exponent as a function of p is discussed, as is an application to branching processes in a random environment.     

Monotone and Convex C 1 Hermite Interpolants Generated by a Subdivision Scheme

   Abstract. We propose C ¹ Hermite interpolants generated by the general subdivision scheme introduced by Merrien [17] and satisfying monotonicity or convexity constraints. For arbitrary values and slopes of a given function f at the end-points of a bounded interval, which are compatible with the contraints, the given algorithms construct shape-preserving interpolants. Moreover, these algorithms are quite simple and fast as well as adapted to CAGD. We also give error estimates in the case of interpolation of smooth functions.     

On the ( r | X p )-Medianoid Problem on a Network with Vertex and Edge Demands

This article considers the (r|X _p )-medianoid problem on a network N=(V,E) with vertex and edge demands. There are already p facilities located on the network and customers patronize the closest facility. The aim is to locate r additional facilities on the network where their captured demands will be maximized. Relationships with the (r|X _p )-medianoid problem with vertex demands are established. Complexity and algorithmic results are presented.     

Nondifferentiable Minimax Fractional Programming Problems with (C, α, ρ, d)-Convexity

In this paper, we present necessary optimality conditions for nondifferentiable minimax fractional programming problems. A new concept of generalized convexity, called (C, α, ρ, d)-convexity, is introduced. We establish also sufficient optimality conditions for nondifferentiable minimax fractional programming problems from the viewpoint of the new generalized convexity. When the sufficient conditions are utilized, the corresponding duality theorems are derived for two types of dual programs. This research was partially supported by NSF and Air Force grants     

Asymptotic Completeness for Multiparticle Dispersive Charge Transfer Models

We prove the asymptotic completeness of wave operators associated with the scattering of a quantum particle in a field of classical particles in the dispersive case when the free hamiltonian has the formH₀=p(D) withpelliptic of degreem?1 and satisfying some convexity hypotheses.