A hybrid conjugate finite-step length method for robust and efficient reliability analysis

The robustness and efficiency of the first-order reliability method (FORM) are the important issues in the structural reliability analysis. In this paper, a hybrid conjugate search direction with finite-step length is proposed to improve the efficiency and robustness of FORM, namely hybrid conjugate finite-step length (CFSL-H). The conjugate scalar factor in CFSL-H is adaptively updated using two conjugate methods with a dynamic participation factor. The accuracy, efficiency and robustness of the CFSL-H are illustrated through the nonlinear explicit and structural implicit limit state functions with normal and non-normal random variables. The results illustrated that the proposed CFSL-H algorithm is more robust, efficient and accurate than the modified existing FORM algorithms for complex structural problems.     

A robust and efficient estimation method for single-index varying-coefficient models

A new estimation procedure based on modal regression is proposed for single-index varying-coefficient models. The proposed method achieves better robustness and efficiency than that of Xue and Pang (2013). We establish the asymptotic normalities of proposed estimators and evaluate the performance of the proposed method by a numerical simulation.     

On a robust and efficient maximum depth estimator

The best breakdown point robustness is one of the most outstanding features of the univariate median. For this robustness property, the median, however, has to pay the price of a low efficiency at normal and other light-tailed models. Affine equivariant multivariate analogues of the univariate median with high breakdown points were constructed in the past two decades. For the high breakdown robustness, most of them also have to sacrifice their efficiency at normal and other models, nevertheless. The affine equivariant maximum depth estimator proposed and studied in this paper turns out to be an exception. Like the univariate median, it also possesses a highest breakdown point among all its multivariate competitors. Unlike the univariate median, it is also highly efficient relative to the sample mean at normal and various other distributions, overcoming the vital low-efficiency shortcoming of the univariate and other multivariate generalized medians. The paper also studies the asymptotics of the estimator and establishes its limit distribution without symmetry and other strong assumptions that are typically imposed on the underlying distribution. This work was supported by Natural Science Foundation of USA (Grant Nos. DMS-0071976, DMS-0234078) and by the Southwestern University of Finance and Economics Third Period Construction Item Funds of the 211 Project (Grant No. 211D3T06)     

A robust and efficient method for solving point distance problems by homotopy

Very large nonlinear unconstrained binary optimization problems arise in a broad array of applications. Several exact or heuristic techniques have proved quite successful for solving many of these problems when the objective function is a quadratic polynomial. However, no similarly efficient methods are available for the higher degree case. Since high degree objectives are becoming increasingly important in certain application areas, such as computer vision, various techniques have been recently developed to reduce the general case to the quadratic one, at the cost of increasing the number of variables by introducing additional auxiliary variables. In this paper we initiate a systematic study of these quadratization approaches. We provide tight lower and upper bounds on the number of auxiliary variables needed in the worst-case for general objective functions, for bounded-degree functions, and for a restricted class of quadratizations. Our upper bounds are constructive, thus yielding new quadratization procedures. Finally, we completely characterize all “minimal” quadratizations of negative monomials.     

Joint spare parts inventory and reliability decisions under a service constraint

Reliability and inventory levels of spare parts are major factors that determine the service level for the maintenance of machines provided by original equipment manufacturers (OEMs). In general, decisions on reliability and stock levels are made separately in practice, and academic literature offers little guidance on how to jointly make these two decisions. In order to fill in the gap in the literature and provide guidance to OEMs, we jointly model reliability and inventory problems. We consider three different service measures: aggregate fill rate, average downtime per system per year and expected total number of long downs in a year. Our models minimize the sum of holding and emergency shipment costs subject to a limited reliability improvement budget and a target service level. We develop an algorithm that considers reliability and inventory decisions simultaneously, test our solution approach on real-life and randomly generated data sets and compare the results with an approach that considers reliability and inventory decisions sequentially. Numerical results show substantial benefits of integrating reliability and inventory decisions.     

Distributionally robust chance constraint with unimodality-skewness information and conic reformulation

Moment-based ambiguity sets are mostly used in distributionally robust chance constraints (DRCCs). Their conservatism can be reduced by imposing unimodality, but the known reformulations do not scale well. We propose a new ambiguity set tailored to unimodal and seemingly symmetric distributions by encoding unimodality-skewness information, which leads to conic reformulations of DRCCs that are more tractable than known ones based on semi-definite programs. Besides, the conic reformulation yields a closed-form expression of the inverse of unimodal Cantelli's bound.     

An efficient method for solving nonlocal initial-boundary value problems for linear and nonlinear first-order hyperbolic partial differential equations

In this paper, we present a new approach to solve nonlocal initial-boundary value problems of linear and nonlinear hyperbolic partial differential equations of first-order subject to initial and nonlocal boundary conditions of integral type. We first transform the given nonlocal initial-boundary value problems into local initial-boundary value problems. Then we apply a modified Adomian decomposition method, which permits convenient resolution of these problems. Moreover, we prove this decomposition scheme applied to such nonlocal problems is convergent in a suitable Hilbert space, and then extend our discussion to include systems of first-order linear equations and other related nonlocal initial-boundary value problems.     

Estimation of a non-negative function

It is pointed out that the problem of estimating a function from the values of a finite number of functionals is often posed, at least partially, as an aesthetic problem rather than as a mathematical one.A particular problem of interpretation of experimental results, arising in the study of radiation source distribution in a plasma, is formulated mathematically in two different ways. Although the two formulations are different in character, they both rely on the idea of imposing a relative likelihood distribution on an appropriate function space; this is particularly fruitful when observational errors are taken into account.     

Weak coupling of solutions of first-order least-squares method

A theoretical analysis of a first-order least-squares finite element method for second-order self-adjoint elliptic problems is presented. We investigate the coupling effect of the approximate solutions for the primary function and for the flux . We prove that the accuracy of the approximate solution for the primary function is weakly affected by the flux . That is, the bound for is dependent on , but only through the best approximation for multiplied by a factor of meshsize . Similarly, we provide that the bound for is dependent on , but only through the best approximation for multiplied by a factor of the meshsize . This weak coupling is not true for the non-selfadjoint case. We provide the numerical experiment supporting the theorems in this paper.

     

A robust and efficient algorithm for planar competitive location problems

In this paper we empirically analyze several algorithms for solving a Huff-like competitive location and design model for profit maximization in the plane. In particular, an exact interval branch-and-bound method and a multistart heuristic already proposed in the literature are compared with uego (Universal Evolutionary Global Optimizer), a recent evolutionary algorithm. Both the multistart heuristic and uego use a Weiszfeld-like algorithm as local search procedure. The computational study shows that uego is superior to the multistart heuristic, and that by properly fine-tuning its parameters it usually (in the computational study, always) find the global optimal solution, and this in much less time than the interval branch-and-bound method. Furthermore, uego can solve much larger problems than the interval method.     

All first-order averaging techniques for a posteriori finite element error control on unstructured grids are efficient and reliable

All first-order averaging or gradient-recovery operators for lowest-order finite element methods are shown to allow for an efficient a posteriori error estimation in an isotropic, elliptic model problem in a bounded Lipschitz domain in . Given a piecewise constant discrete flux (that is the gradient of a discrete displacement) as an approximation to the unknown exact flux (that is the gradient of the exact displacement), recent results verify efficiency and reliability of

in the sense that is a lower and upper bound of the flux error up to multiplicative constants and higher-order terms. The averaging space consists of piecewise polynomial and globally continuous finite element functions in components with carefully designed boundary conditions. The minimal value is frequently replaced by some averaging operator applied within a simple post-processing to . The result provides a reliable error bound with .

This paper establishes and so equivalence of and . This implies efficiency of for a large class of patchwise averaging techniques which includes the ZZ-gradient-recovery technique. The bound established for tetrahedral finite elements appears striking in that the shape of the elements does not enter: The equivalence is robust with respect to anisotropic meshes. The main arguments in the proof are Ascoli's lemma, a strengthened Cauchy inequality, and elementary calculations with mass matrices.

     

A robust and efficient adaptive reweighted estimator of multivariate location and scatter

This article proposes a reweighted estimator of multivariate location and scatter, with weights adaptively computed from the data. Its breakdown point and asymptotic behavior under elliptical distributions are established. This adaptive estimator is able to attain simultaneously the maximum possible breakdown point for affine equivariant estimators and full asymptotic efficiency at the multivariate normal distribution. For the special case of hard-rejection weights and the MCD as initial estimator, it is shown to be more efficient than its non-adaptive counterpart for a broad range of heavy-tailed elliptical distributions. A Monte Carlo study shows that the adaptive estimator is as robust as its non-adaptive relative for several types of bias-inducing contaminations, while it is remarkably more efficient under normality for sample sizes as small as 200.     

On a transport problem and monoids of non-negative integers

A problem about how to transport profitably a group of cars leads us to studying the set T formed by the integers n such that the system of inequalities, with non-negative integer coefficients,

$$\begin{aligned} a_1x_1 +\cdots + a_px_p + \alpha \le n \le b_1x_1 +\cdots + b_px_p - \beta \end{aligned}$$

has at least one solution in \({\mathbb N}^p\). We prove that \(T\cup \{0\}\) is a submonoid of \(({\mathbb N},+)\) and, moreover, we give algorithmic processes to compute T.

     

Optimality conditions and a barrier method in optimization with convex geometric constraint

In this note we address a new look to some questions raised by Lasserre in his works (Optim. Lett. 4:1–5, 2010, Optim. Lett. 5:549–556, 2011), concerning the preservation of the conclusions of some results in smooth convex optimization with inequalities constraints to the case where the feasible set is convex, but has no convex representation. The main results we show concern, on one hand, some relations between the hypotheses imposed by Lasserre and the Mangasarian–Fromowitz condition, and, on the other hand, a barrier method based only on the geometric representation of the feasible set.     

On a characterization of irreducibility of a non-negative matrix

Let A denote an n×n matrix with all its elements real and non-negative, and let r_i be the sum of the elements in the ith row of A, i=1,…,n. Let B=A?D(r₁,…,r_n), where D(r₁,…,r_n) is the diagonal matrix with r_i at the position (i,i). Then it is proved that A is irreducible if and only if rank B=n?1 and the null space of B^T contains a vector d whose entries are all non-null.