On a second order parallel variable transformation approach

In this paper we present a second order PVT (parallel variable transformation) algorithm converging to second order stationary points for minimizing smooth functions, based on the first order PVT algorithm due to Fukushima (1998). The corresponding stopping criterion, descent condition and descent step for the second order PVT algorithm are given.     

Integrated inventory problem under trade credit in fuzzy random environment

This paper is concerned with an integrated inventory problem under trade credit where both the demand rate and deteriorating rate are assumed to be uncertain and characterized as fuzzy random variables with known distributions. The objective of this paper is to determine the optimal inventory policy by optimizing simultaneously the replenishment cycle length and trade credit period. At first, three decision criteria are given: (1) expected value criterion, (2) chance-constrained criterion and (3) chance maximization criterion. Then, after building the fuzzy random models based on the above decision criterion, a hybrid intelligent algorithm by integrating fuzzy random simulation and genetic algorithm is employed to deal with these models. At the end, three numerical examples are given to illustrate the benefits of the models and show the effectiveness of the algorithms.     

Ensuring population diversity in genetic algorithms: A technical note with application to the cell formation problem

The entropy-based measure has been used in previous works to compute the population diversity in solving the cell formation problem with the genetic algorithm. Population diversity is crucial to the genetic algorithm’s ability to continue fruitful exploration as it may be used in choosing an initial population, in defining a stopping criterion, in evaluating the population convergence, and in making the search more efficient throughout the selection of crossover operators or the adjustment of various control parameters (e.g., crossover or mutation rate, population size). We show in this note that, when a non-ordinal chromosome representation corresponding to the allocation of machines to cells is used, the current way of measuring the population diversity is inaccurate. Consequently, it leads to wrong conclusions when, at various iterations, carrying out fruitful exploration or an efficient search of the solution space is guided by the perceived population diversity degree. An alternative approach based on computing the distance and the similarity between chromosomes is discussed.     

An alternating method for an inverse Cauchy problem

In this paper, an iterative boundary element method based on our relaxed algorithm introduced in [8] is used to solve numerically a class of inverse boundary problems. A dynamical choice of the relaxation parameter is presented and a stopping criterion based on our theoretical results is used. The numerical results show that the algorithm produces a reasonably approximate solution and improves the rate of convergence of Kozlov's scheme [10]. This revised version was published online in June 2006 with corrections to the Cover Date.     

A new stopping criterion for linear perturbed asynchronous iterations

A new stopping criterion is proposed for asynchronous linear fixed point methods in finite precision. The case of absolute error is considered. The originality of this stopping criterion relies on the fact that all tests are made within the same macroiteration.     

Evolutionary multiobjective optimization in noisy problem environments

This paper presents a multiobjective evolutionary algorithm (MOEA) capable of handling stochastic objective functions. We extend a previously developed approach to solve multiple objective optimization problems in deterministic environments by incorporating a stochastic nondomination-based solution ranking procedure. In this study, concepts of stochastic dominance and significant dominance are introduced in order to better discriminate among competing solutions. The MOEA is applied to a number of published test problems to assess its robustness and to evaluate its performance relative to NSGA-II. Moreover, a new stopping criterion is proposed, which is based on the convergence velocity of any MOEA to the true Pareto optimal front, even if the exact location of the true front is unknown. This stopping criterion is especially useful in real-world problems, where finding an appropriate point to terminate the search is crucial.     

Selection of a correlated equilibrium in Markov stopping games

This paper deals with an extension of the concept of correlated strategies to Markov stopping games. The Nash equilibrium approach to solving nonzero-sum stopping games may give multiple solutions. An arbitrator can suggest to each player the decision to be applied at each stage based on a joint distribution over the players’ decisions according to some optimality criterion. This is a form of equilibrium selection. Examples of correlated equilibria in nonzero-sum games related to the best choice problem are given. Several concepts of criteria for selecting a correlated equilibrium are used.     

A nonmonotone truncated Newton–Krylov method exploiting negative curvature directions, for large scale unconstrained optimization

We propose a new truncated Newton method for large scale unconstrained optimization, where a Conjugate Gradient (CG)-based technique is adopted to solve Newton’s equation. In the current iteration, the Krylov method computes a pair of search directions: the first approximates the Newton step of the quadratic convex model, while the second is a suitable negative curvature direction. A test based on the quadratic model of the objective function is used to select the most promising between the two search directions. Both the latter selection rule and the CG stopping criterion for approximately solving Newton’s equation, strongly rely on conjugacy conditions. An appropriate linesearch technique is adopted for each search direction: a nonmonotone stabilization is used with the approximate Newton step, while an Armijo type linesearch is used for the negative curvature direction. The proposed algorithm is both globally and superlinearly convergent to stationary points satisfying second order necessary conditions. We carry out a significant numerical experience in order to test our proposal.     

Displacement or Residual Test in the Application of Implicit Methods for Stiff Problems

The stopping criterion in the iterative solution of the non-linearequations that arise in connection with the use of implicitmethods for the solution of stiff systems of ordinary differentialequations is studied. The conclusion is that there must be aconsistent choice between the type of error estimator and thevector used for the test of convergence and stopping criterion.     

Inexact Inverse Iteration for Generalized Eigenvalue Problems

In this paper, we study an inexact inverse iteration with inner-outer iterations for solving the generalized eigenvalu problem Ax = Bx, and analyze how the accuracy in the inner iterations affects the convergence of the outer iterations. By considering a special stopping criterion depending on a threshold parameter, we show that the outer iteration converges linearly with the inner threshold parameter as the convergence rate. We also discuss the total amount of work and asymptotic equivalence between this stopping criterion and a more standard one. Numerical examples are given to illustrate the theoretical results.     

A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models

In this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online.     

Optimal Learning Rates for Kernel Partial Least Squares

We study two learning algorithms generated by kernel partial least squares (KPLS) and kernel minimal residual (KMR) methods. In these algorithms, regularization against overfitting is obtained by early stopping, which makes stopping rules crucial to their learning capabilities. We propose a stopping rule for determining the number of iterations based on cross-validation, without assuming a priori knowledge of the underlying probability measure, and show that optimal learning rates can be achieved. Our novel analysis consists of a nice bound for the number of iterations in a priori knowledge-based stopping rule for KMR and a stepping stone from KMR to KPLS. Technical tools include a recently developed integral operator approach based on a second order decomposition of inverse operators and an orthogonal polynomial argument.     

Boundary element-Landweber method for the Cauchy problem in linear elasticity

In this paper, an iterative algorithm based on the Landwebermethod in combination with the boundary element method is developedfor solving the Cauchy problem in isotropic linear elasticity.An efficient regularizing stopping criterion is also employed.The numerical results obtained confirm that the iterative methodproduces a convergent and stable numerical solution with respectto increasing the number of boundary elements and decreasingthe amount of noise added into the input data, respectively.     

An optimally convergent adaptive mixed finite element method

We prove convergence and optimal complexity of an adaptive mixed finite element algorithm, based on the lowest-order Raviart–Thomas finite element space. In each step of the algorithm, the local refinement is either performed using simple edge residuals or a data oscillation term, depending on an adaptive marking strategy. The inexact solution of the discrete system is controlled by an adaptive stopping criterion related to the estimator.     

求解方程的迭代过程中算法停止准则的讨论

分析了在求解方程的迭代过程中,算法中常用单一停止准则的不足,并给出了一些组合的停止准则,数值实验表明,这些停止准则是有效的.     

Risk-sensitive stopping problems for continuous-time Markov chains

In this paper we consider stopping problems for continuous-time Markov chains under a general risk-sensitive optimization criterion for problems with finite and infinite time horizon. More precisely our aim is to maximize the certainty equivalent of the stopping reward minus cost over the time horizon. We derive optimality equations for the value functions and prove the existence of optimal stopping times. The exponential utility is treated as a special case. In contrast to risk-neutral stopping problems it may be optimal to stop between jumps of the Markov chain. We briefly discuss the influence of the risk sensitivity on the optimal stopping time and consider a special house selling problem as an example.     

On a new stopping rule for stochastic approximation

Summary A new stopping rule for the Robbins-Monro process, based on an F-statistic criterion is proposed and its asymptotic behavior established. On the basis of evidence obtained through experimental sampling, the procedure seems to work well over a wide variety of situations. A two-stage procedure, coupling the new rule with an earlier one proposed by Sielken [1973] is recommended for practical use.     

The Cauchy problem for Laplace's equation via the conjugate gradient method

A variational formulation of the Cauchy problem for the Laplaceequation is studied. An efficient conjugate gradient method based on an optimal-order stopping criterion is presented together with its numerical implementation based on the boundary-element method. Several numerical examples involving smooth or non-smooth geometries and over-, equally, or under-specified Cauchy dataare discussed. The numerical results show that the numericalsolution is convergent with respect to increasing the numberof boundary elements and stable with respect to decreasingthe amount of noise included in the input Cauchy data. Received 2 November, 1999. Revised 4 March, 2000.     

Multicriteria tour planning for mobile healthcare facilities in a developing country

A multiobjective combinatorial optimization (MOCO) formulation for the following location-routing problem in healthcare management is given: For a mobile healthcare facility, a closed tour with stops selected from a given set of population nodes has to be found. Tours are evaluated according to three criteria: (i) An economic efficiency criterion related to the tour length, (ii) the criterion of average distances to the nearest tour stops corresponding to p-median location problem formulations, and (iii) a coverage criterion measuring the percentage of the population unable to reach a tour stop within a predefined maximum distance. Three algorithms to compute approximations to the set of Pareto-efficient solutions of the described MOCO problem are developed. The first uses the P-ACO technique, and the second and the third use the VEGA and the MOGA variant of multiobjective genetic algorithms, respectively. Computational experiments for the Thiès region in Senegal were carried out to evaluate the three approaches on real-world problem instances.     

Sufficient Optimality Criterion for Linearly Constrained,Separable Concave Minimization Problems

A sufficient optimality criterion for linearly-constrained concave minimization problems is given in this paper. Our optimality criterion is based on the sensitivity analysis of the relaxed linear programming problem. The main result is similar to that of Phillips and Rosen (Ref. 1); however, our proofs are simpler and constructive.In the Phillips and Rosen paper (Ref. 1), they derived a sufficient optimality criterion for a slightly different linearly-constrained concave minimization problem using exponentially many linear programming problems. We introduce special test points and, using these for several cases, we are able to show optimality of the current basic solution.The sufficient optimality criterion described in this paper can be used as a stopping criterion for branch-and-bound algorithms developed for linearly-constrained concave minimization problems.This research was supported by a Bolyai János Research Fellowship BO/00334/00 of the Hungarian Academy of Science and by the Hungarian Scientific Research Foundation, Grant OTKA T038027.