An Inverse Problem in Periodic Diffractive Optics: Reconstruction of Lipschitz Grating Profiles

We consider the problem of recovering a two-dimensional periodic structure from scattered waves measured above the structure. Following an approach by Kirsch and Kress, this inverse problem is reformulated as a nonlinear optimization problem. We develop a theoretical basis for the reconstruction method in the case of an arbitrary Lipschitz grating profile. The convergence analysis is based on new perturbation and stability results for the forward problem.     

A two dimensional Euler–Lagrangian model of wood gasification in a charcoal bed—Part II: Parameter influence and comparison

A Euler–Lagrangian simulation was employed for a comprehensive parameter study of wood gasification in a fluidized charcoal bed. The parameters that were varied include the initial bed temperature, fuel mass flow rate, inert tar fraction, and kinetic energy losses caused by particle–particle and particle–wall collisions. The results of each parameter variation are compared with a base scenario, previously described in detail in Part I of this study (Gerber & Oevermann, 2014). The results are interpreted by comparing the reactor outlet temperature, averaged particle temperature, overall wood mass, overall charcoal mass, concentrations of several gaseous species, and axial barycenter data for particles obtained with different sets of parameters. The inert tar fraction and fuel mass flow rate are the most sensitive parameter, while the particle–particle and particle–wall contact parameters have only a small impact on the results. Increasing the reactive tar components by 19% almost doubled the amount of reactive tars at the reactor outlet, while decreasing the restitution coefficients of the particle collisions by 0.2 results in higher overall gas production but almost no change in bed height. Herein, our numerical results are discussed in detail while assessing the model restrictions.     

On safe tractable approximations of chance constraints

A natural way to handle optimization problem with data affected by stochastic uncertainty is to pass to a chance constrained version of the problem, where candidate solutions should satisfy the randomly perturbed constraints with probability at least 1 − ?. While being attractive from modeling viewpoint, chance constrained problems “as they are” are, in general, computationally intractable. In this survey paper, we overview several simulation-based and simulation-free computationally tractable approximations of chance constrained convex programs, primarily, those of chance constrained linear, conic quadratic and semidefinite programming.     

The duality property of the Discrete Fourier Transform based on Simpson's rule

The classical Discrete Fourier Transform (DFT) satisfies a duality property that transforms a discrete time signal to the frequency domain and back to the original domain. In doing so, the original signal is reversed to within a multiplicative factor, namely the dimension of the transformation matrix. In this paper, we prove that the DFT based on Simpson's method satisfies a similar property and illustrate its effect on a real discrete signal. The duality property is particularly useful in determining the components of the transformation matrix as well as components of its positive integral powers. Copyright © 2012 John Wiley & Sons, Ltd.     

An abstract symmetric framework for duality in mathematical programming

The paper offers an abstract structure called environment (of mathematical programming) in which a pair of dual programs is settled symmetrically. Given an environment, the concepts of an optimization program, its dual and the associated Lagrangean are formalized and studied. The usual asymmetries in certain types of mathematical programming are due to selection of asymmetric environments. Common types of programming like: linear, nonlinear, convex, integer etc. are reviewed in the proposed framework.     

Weierstrass theorem for monotonically semicontinuous functions

The concept of monotone semicontinuity is introduced. It is shown that every monotonically semicontinuous function with values in a space equipped with arbitrary preference relation achieves its extremes on compacts     

Analysis for some properties of discrete time Markov decision processes

This paper investigates properties of the optimality equation and optimal policies in discrete time Markov decision processes with expected discounted total rewards under weak conditions that the model is well defined and the optimality equation is true. The optimal value function is characterized as a solution of the optimality equation and the structure of optimal policies is also given.     

The multi-attribute grey target decision method for attribute value within three-parameter interval grey number

Based on the grey system theory and methods, the grey-target decision-making problem is discussed, in which the attribute values are grey numbers and the maximum probability of the value of grey number is known. Firstly, the optimal effect vector is the positive bull’s-eye and positive bull’s-eye distance of each scheme is defined. Subjectively or objectively weighting method is integrated to determine the index weight and integrated optimization model of index weight is established. Finally, the critical effect vector is the negative bull’s-eye and negative bull’s-eye distance of each scheme is defined, then relative bull’s-eye distance and comprehensive the bull’s-eye distance of grey target decision-making are given. An example is also presented to illustrate the usefulness and effectiveness of the methods obtained in this paper and provides a new idea for grey target decision-making method research.     

An evaluation of semidefinite programming based approaches for discrete lot-sizing problems

The present work is intended as a first step towards applying semidefinite programming models and tools to discrete lot-sizing problems including sequence-dependent changeover costs and times. Such problems can be formulated as quadratically constrained quadratic binary programs. We investigate several semidefinite relaxations by combining known reformulation techniques recently proposed for generic quadratic binary problems with problem-specific strengthening procedures developed for lot-sizing problems. Our computational results show that the semidefinite relaxations consistently provide lower bounds of significantly improved quality as compared with those provided by the best previously published linear relaxations. In particular, the gap between the semidefinite relaxation and the optimal integer solution value can be closed for a significant proportion of the small-size instances, thus avoiding to resort to a tree search procedure. The reported computation times are significant. However improvements in SDP technology can still be expected in the future, making SDP based approaches to discrete lot-sizing more competitive.