Second-order sequence-based necessary optimality conditions in constrained nonsmooth vector optimization and applications

Several notions of sequential directional derivatives and sequential local approximations are introduced. Under (first-order) Hadamard differentiability assumptions of the data at the point of study, these concepts are utilized to analyze second-order necessary optimality conditions, which rely on given sequences, for local weak solutions in nonsmooth vector optimization problems with constraints. Some applications to minimax programming problems are also derived.     

Modal characterisation theorems over special classes of frames

We investigate model theoretic characterisations of the expressive power of modal logics in terms of bisimulation invariance. The paradigmatic result of this kind is van Benthem’s theorem, which says that a first-order formula is invariant under bisimulation if, and only if, it is equivalent to a formula of basic modal logic. The present investigation primarily concerns ramifications for specific classes of structures. We study in particular model classes defined through conditions on the underlying frames, with a focus on frame classes that play a major role in modal correspondence theory and often correspond to typical application domains of modal logics. Classical model theoretic arguments do not apply to many of the most interesting classes-for instance, rooted frames, finite rooted frames, finite transitive frames, well-founded transitive frames, finite equivalence frames-as these are not elementary. Instead we develop and extend the game-based analysis (first-order Ehrenfeucht-Fraïssé versus bisimulation games) over such classes and provide bisimulation preserving model constructions within these classes. Over most of the classes considered, we obtain finite model theory analogues of the classically expected characterisations, with new proofs also for the classical setting. The class of transitive frames is a notable exception, with a marked difference between the classical and the finite model theory of bisimulation invariant first-order properties. Over the class of all finite transitive frames in particular, we find that monadic second-order logic is no more expressive than first-order as far as bisimulation invariant properties are concerned — though both are more expressive here than basic modal logic. We obtain ramifications of the de Jongh-Sambin theorem and a new and specific analogue of the Janin-Walukiewicz characterisation of bisimulation invariant monadic second-order for finite transitive frames.     

Contingent derivatives and regularization for noncoercive inverse problems

Abstract

We study the inverse problem of parameter identification in noncoercive variational problems that commonly appear in applied models. We examine the differentiability of the set-valued parameter-to-solution map using the first-order and the second-order contingent derivatives. We explore the inverse problem using the output least-squares and the modified output least-squares objectives. By regularizing the noncoercive variational problem, we obtain a single-valued regularized parameter-to-solution map and investigate its smoothness and boundedness. We also consider optimization problems using the output least-squares and the modified output least-squares objectives for the regularized variational problem. We give a complete convergence analysis showing that for the output least-squares and the modified output least-squares, the regularized minimization problems approximate the original optimization problems suitably. We also provide the first-order and the second-order adjoint method for the computation of the first-order and the second-order derivatives of the output least-squares objective. We provide discrete formulas for the gradient and the Hessian calculation and present numerical results.     

Crowding effects promote coexistence in the chemostat

This paper deals with an almost-global stability result for a particular chemostat model. It deviates from the classical chemostat because crowding effects are taken into consideration. This model can be rewritten as a negative feedback interconnection of two systems which are monotone (as input/output systems). Moreover, these subsystems behave nicely when subject to constant inputs. This allows the use of a particular small-gain theorem which has recently been developed for feedback interconnections of monotone systems. Application of this theorem requires—at least approximate—knowledge of two gain functions associated to the subsystems. It turns out that for the chemostat model proposed here, these approximations can be obtained explicitly and this leads to a sufficient condition for almost-global stability. In addition, we show that coexistence occurs in this model if the crowding effects are large enough.     

Are economically advanced countries more efficient in basic and applied research?

Research and development (R&D) of countries play a major role in a long-term development of the economy. We measure the R&D efficiency of all 28 member countries of the European Union in the years 2008–2014. Super-efficient data envelopment analysis (DEA) based on robustness of classification into efficient and inefficient units is adopted. We use the number of citations as output of basic research, the number of patents as output of applied research and R&D expenditures with manpower as inputs. To meet DEA assumptions and to capture R&D characteristics, we analyze a homogeneous sample of countries, adjust prices using purchasing power parity and consider time lag between inputs and outputs. We find that the efficiency of general R&D is higher for countries with higher GDP per capita. This relation also holds for specialized efficiencies of basic and applied research. However, it is much stronger for applied research suggesting its outputs are more easily distinguished and captured. Our findings are important in the evaluation of research and policy making.     

Weber Correspondence Analysis: The One-Dimensional Case

When designing programs or software for the implementation of Monte Carlo (MC) hypothesis tests, we can save computation time by using sequential stopping boundaries. Such boundaries imply stopping resampling after relatively few replications if the early replications indicate a very large or a very small p value. We study a truncated sequential probability ratio test (SPRT) boundary and provide a tractable algorithm to implement it. We review two properties desired of any MC p value, the validity of the p value and a small resampling risk, where resampling risk is the probability that the accept/reject decision will be different than the decision from complete enumeration. We show how the algorithm can be used to calculate a valid p value and confidence intervals for any truncated SPRT boundary. We show that a class of SPRT boundaries is minimax with respect to resampling risk and recommend a truncated version of boundaries in that class by comparing their resampling risk (RR) to the RR of fixed boundaries with the same maximum resample size. We study the lack of validity of some simple estimators of p values and offer a new, simple valid p value for the recommended truncated SPRT boundary. We explore the use of these methods in a practical example and provide the MChtest R package to perform the methods.     

Information theory and alternate-hypothesis tests

Lindley's concept of the information in an experiment is used to study alternate-hypothesis statistical tests. A test is considered to be a combination of a sampling rule and a decision rule which is based on the samples taken. Two information functions are formed: the information about the unknown parameter contained in the samples taken and the information contained in the decision to which these samples lead. The former quantity is at least as great as the latter, with equality if, and only if, the test is an SPRT (sequential probability ratio test) neglecting the excess over the boundary.If the average information contained in the samples taken in one alternate-hypothesis test is greater than the average information contained in the samples taken in another, then the first test has greater expected length. It is also shown that an SPRT neglecting excess is the unique alternate-hypothesis test that utilizes all the information contained in the samples taken. These results provide a proof of the standard result concerning the optimal character of the SPRT neglecting excess.The major purpose of this paper is to develop an information-theoretic analysis of alternate-hypothesis tests. By means of this approach, new results on the SPRT and alternate-hypothesis tests in general are obtained.     

计数型二次序贯网图检验

序贯概率比检验(SPRT)是应用非常广泛的抽样检验方法, 序贯网图检验在控制最大样本量方面很好地改进了SPRT, 但其结果还有进一步改进的余地, 为此, 我们建立了二次序贯网图检验, 计算结果表明, 它比原先的计数序贯网图检验有更好的效果.     

Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping

This paper proposes a novel method to select an experimental design for interpolation in random simulation, especially discrete event simulation. (Though the paper focuses on Kriging, this design approach may also apply to other types of metamodels such as non-linear regression models and splines.) Assuming that simulation requires much computer time, it is important to select a design with a small number of observations (or simulation runs). The proposed method is therefore sequential. Its novelty is that it accounts for the specific input/output behavior (or response function) of the particular simulation at hand; i.e., the method is customized or application-driven. A tool for this customization is bootstrapping, which enables the estimation of the variances of predictions for inputs not yet simulated. The method is tested through two classic simulation models, namely the expected steady-state waiting time of the M/M/1 queuing model, and the mean costs of a terminating (s, S) inventory simulation. For these two simulation models the novel design indeed gives better results than a popular alternative design, namely Latin Hypercube Sampling (LHS) with a prefixed sample.     

Inexact Josephy–Newton framework for generalized equations and its applications to local analysis of Newtonian methods for constrained optimization

We propose and analyze a perturbed version of the classical Josephy–Newton method for solving generalized equations. This perturbed framework is convenient to treat in a unified way standard sequential quadratic programming, its stabilized version, sequential quadratically constrained quadratic programming, and linearly constrained Lagrangian methods. For the linearly constrained Lagrangian methods, in particular, we obtain superlinear convergence under the second-order sufficient optimality condition and the strict Mangasarian–Fromovitz constraint qualification, while previous results in the literature assume (in addition to second-order sufficiency) the stronger linear independence constraint qualification as well as the strict complementarity condition. For the sequential quadratically constrained quadratic programming methods, we prove primal-dual superlinear/quadratic convergence under the same assumptions as above, which also gives a new result.     

High-resolution schemes for bubbling flow computations

This paper is concerned with the effects of numerical schemes on the simulation of dense gas-particle two-phase flows. The first-order upwind difference, the central difference, the second-order upwind difference, the central difference plus artificial dissipation, the deferred correction, the quadratic upstream interpolation (for convective kinematics) and the monotone upstream-centered schemes for conservation laws with different flux limiters were all accomplished to simulate the multi-phase flows. It was found that numerical schemes may significantly affect the solution accuracy and numerical convergence. The monotone upstream-centered schemes for conservation laws are the best choice of all, because they can effectively suppress the non-physical oscillations with the introduction of adaptive numerical dissipation into numerical solutions.     

Stabilized sequential quadratic programming for optimization and a stabilized Newton-type method for variational problems

The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve fast convergence despite possible degeneracy of constraints of optimization problems, when the Lagrange multipliers associated to a solution are not unique. Superlinear convergence of sSQP had been previously established under the strong second-order sufficient condition for optimality (without any constraint qualification assumptions). We prove a stronger superlinear convergence result than the above, assuming the usual second-order sufficient condition only. In addition, our analysis is carried out in the more general setting of variational problems, for which we introduce a natural extension of sSQP techniques. In the process, we also obtain a new error bound for Karush–Kuhn–Tucker systems for variational problems that holds under an appropriate second-order condition.     

A more accurate second-order polynomial metamodel using a pseudo-random number assignment strategy

The response surface metamodel is a useful sequential methodology for approximating the relationship between the input variables and the output response in computer simulation. Several strategies have been proposed to increase the accuracy of the estimation of the metamodel. In the current paper, we introduce an effective pseudo-random number (PRN) assignment strategy with Box-Behnken design to construct a more accurate second-order polynomial metamodel to estimate the network reliability of a complex system. The results obtained from the simulation approach show that the reduction in maximum absolute relative error between the response surface approximation and the actual reliability function is 35.63% after the PRN assignment strategy is applied.     

Initial transient detection in simulations using the second-order cumulant spectrum

Procedures for detecting an initial transient in simulation output data are developed. The tests use the second-order cumulant spectrum which differs from the power spectrum in that the stationarity constraint is not required for the former. The second-order cumulant spectrum can be interpreted as the nonstationary power spectrum and is an orthogonal decomposition of the variance of a nonstationary process. The null hypothesis is that the simulation output data series is a covariance stationary process. Equivalently, all estimates of the second-order cumulant spectrum in the region which excludes the estimates of the power spectrum will have an expected value of zero. The test procedures are designed to detect initialization bias in the estimation of the mean and the variance. These procedures can be extended to detect bias in the moments of cumulants of ordern, wheren>2. Results are presented from the application of the test to simulated processes with superimposed mean and variance transients and anM/M/1 queue example.     

Generalizing Mutational Equations for Uniqueness of Some Nonlocal 1<Superscript>st</Superscript>-Order Geometric Evolutions

The mutational equations of Aubin extend ordinary differential equations to metric spaces (with compact balls). In first-order geometric evolutions, however, the topological boundary need not be continuous in the sense of Painlevé–Kuratowski. So this paper suggests a generalization of Aubin’s mutational equations that extends classical notions of dynamical systems and functional analysis beyond the traditional border of vector spaces: Distribution-like solutions are introduced in a set just supplied with a countable family of (possibly non-symmetric) distance functions. Moreover their existence is proved by means of Euler approximations and a form of “weak” sequential compactness (although no continuous linear forms are available beyond topological vector spaces). This general framework is applied to a first-order geometric example, i.e. compact subsets of ? ^N evolving according to the nonlocal properties of both the current set and its proximal normal cones. Here neither regularity assumptions about the boundaries nor the inclusion principle are required. In particular, we specify sufficient conditions for the uniqueness of these solutions.     

How parabolic free boundaries approximate hyperbolic fronts

A rather complete study of the existence and qualitative behaviour of the boundaries of the support of solutions of the Cauchy problem for nonlinear first-order and second-order scalar conservation laws is presented. Among other properties, it is shown that, under appropriate assumptions, parabolic interfaces converge to hyperbolic ones in the vanishing viscosity limit.

     

Generalizing Mutational Equations for Uniqueness of Some Nonlocal 1st-Order Geometric Evolutions

The mutational equations of Aubin extend ordinary differential equations to metric spaces (with compact balls). In first-order geometric evolutions, however, the topological boundary need not be continuous in the sense of Painlevé–Kuratowski. So this paper suggests a generalization of Aubin’s mutational equations that extends classical notions of dynamical systems and functional analysis beyond the traditional border of vector spaces: Distribution-like solutions are introduced in a set just supplied with a countable family of (possibly non-symmetric) distance functions. Moreover their existence is proved by means of Euler approximations and a form of “weak” sequential compactness (although no continuous linear forms are available beyond topological vector spaces). This general framework is applied to a first-order geometric example, i.e. compact subsets of ℝ ^N evolving according to the nonlocal properties of both the current set and its proximal normal cones. Here neither regularity assumptions about the boundaries nor the inclusion principle are required. In particular, we specify sufficient conditions for the uniqueness of these solutions.      

Fourth-Order Splitting Methods for Time-Dependant Differential Equations

This study was suggested by previous work on the simulation of evolution equations with scale-dependent processes,e.g.,wave-propagation or heat-transfer,that are modeled by wave equations or heat equations.Here,we study both parabolic and hyperbolic equations.We focus on ADI (alternating direction implicit) methods and LOD (locally one-dimensional) methods,which are standard splitting methods of lower order,e.g.second-order.Our aim is to develop higher-order ADI methods,which are performed by Richardson extrapolation,Crank-Nicolson methods and higher-order LOD methods,based on locally higher-order methods.We discuss the new theoretical results of the stability and consistency of the ADI methods.The main idea is to apply a higher- order time discretization and combine it with the ADI methods.We also discuss the dis- cretization and splitting methods for first-order and second-order evolution equations. The stability analysis is given for the ADI method for first-order time derivatives and for the LOD (locally one-dimensional) methods for second-order time derivatives.The higher-order methods are unconditionally stable.Some numerical experiments verify our results.     

Examining the effect of second-order terms in mathematical programming approaches to the classification problem

Research on mathematical programming approaches to the classification problem has focused almost exclusively on linear discriminant functions with only first-order terms. While many of these first-order models have displayed excellent classificatory performance when compared to Fisher's linear discriminant method, they cannot compete with Smith's quadratic discriminant method on certain data sets. In this paper, we investigate the appropriateness of including second-order terms in mathematical programming models. Various issues are addressed, such as performance of models with small to moderate sample size, need for crossproduct terms, and loss of power by the mathematical programming models under conditions ideal for the parametric procedures. A simulation study is conducted to assess the relative performance of first-order and second-order mathematical programming models to the parametric procedures. The simulation study indicates that mathematical programming models using polynomial functions may be prone to overfitting on the training samples which in turn may cause rather poor fits on the validation samples. The simulation study also indicates that inclusion of cross-product terms may hurt a polynomial model's accuracy on the validation samples, although omission of them means that the model is not invariant to nonsingular transformations of the data.