Proximal-Point Algorithm Using a Linear Proximal Term

Proximal-point algorithms (PPAs) are classical solvers for convex optimization problems and monotone variational inequalities (VIs). The proximal term in existing PPAs usually is the gradient of a certain function. This paper presents a class of PPA-based methods for monotone VIs. For a given current point, a proximal point is obtained via solving a PPA-like subproblem whose proximal term is linear but may not be the gradient of any functions. The new iterate is updated via an additional slight calculation. Global convergence of the method is proved under the same mild assumptions as the original PPA. Finally, profiting from the less restrictions on the linear proximal terms, we propose some parallel splitting augmented Lagrangian methods for structured variational inequalities with separable operators. B.S. He was supported by NSFC Grant 10571083 and Jiangsu NSF Grant BK2008255.     

Hybrid Conjugate Gradient Algorithm for Unconstrained Optimization

In this paper a new hybrid conjugate gradient algorithm is proposed and analyzed. The parameter β _k is computed as a convex combination of the Polak-Ribière-Polyak and the Dai-Yuan conjugate gradient algorithms, i.e. β _k ^N =(1−θ _k )β _k ^PRP +θ _k β _k ^DY . The parameter θ _k in the convex combination is computed in such a way that the conjugacy condition is satisfied, independently of the line search. The line search uses the standard Wolfe conditions. The algorithm generates descent directions and when the iterates jam the directions satisfy the sufficient descent condition. Numerical comparisons with conjugate gradient algorithms using a set of 750 unconstrained optimization problems, some of them from the CUTE library, show that this hybrid computational scheme outperforms the known hybrid conjugate gradient algorithms. N. Andrei is a member of the Academy of Romanian Scientists, Splaiul Independenţei nr. 54, Sector 5, Bucharest, Romania.     

An Algorithm for the Global Optimization of a Class of Continuous Minimax Problems

We propose an algorithm for the global optimization of continuous minimax problems involving polynomials. The method can be described as a discretization approach to the well known semi-infinite formulation of the problem. We proceed by approximating the infinite number of constraints using tools and techniques from semidefinite programming. We then show that, under appropriate conditions, the SDP approximation converges to the globally optimal solution of the problem. We also discuss the numerical performance of the method on some test problems. Financial support of EPSRC Grant GR/T02560/01 gratefully acknowledged.     

A jaw model for the study of the mandibular flexure taking into account the anisotropy of the bone

Knowledge of the complex biomechanical behaviour of the human mandible is of great importance in various clinical situations. The biomechanical and physical behaviour of mandibles have been investigated by different approaches. Some research have been done to evaluate the functional character of mandibles. Methods such as indirect measurement of deformations performed by intraoral appliances and by holographic interferometry have being employed. Other studies evaluated the mechanical properties and material parameters of small cubes of mandibles. One disadvantage of the experiments using strain gauges or holographic interferometry is the inability to determine strains at defined positions within the specimen. Additionally, research in biomechanics by these methods is limited to surface deformations and neither stresses nor dislocations can be measured.In the course of this study, we have investigated the mandibular flexure under mechanical loads using the results of a Finite Element Analysis (FEA). In order to obtain more accurate and realistic results, the bone anisotropy has being taken into account for the mathematical modelling of the jaw.The objective of this study was to establish a non-invasive procedure to predict precisely the complex biomechanical reactions of mandibles under mechanical loading. In order to achieve this aim, a comparison of the numerical data obtained with the experimental values of previous studies was performed. It showed a good correlation between in vitro measurements and mathematical modelling. Then the Finite Elements (FE) model was used to evaluate some mandibular movements (corporal approximation, dorsoventral shear, and corporal rotation in edentulous subjects).It is concluded that the applied procedure of generating the FE model is a valid and accurate non-invasive method to predict different parameters of the complex biomechanical behaviour of human mandibles.     

Towards auction algorithms for large dense assignment problems

In this paper, we focus on the problem of solving large-scale instances of the linear sum assignment problem by auction algorithms. We introduce a modified auction algorithm, called look-back auction algorithm, which extends the forward auction algorithm by the ability of reusing information from previous bids. We show that it is able to reuse information from the previous bids with high efficiency for all tested types of input instances. We discuss then the design and implementation of a suite of sequential and distributed memory auction algorithms on a Linux cluster with the evaluation on several types of input instances of the linear sum assignment problem. Our results show that the look-back auction algorithm solves sequentially nearly all types of dense instances faster than other evaluated algorithms and it is more stable than the forward-reverse auction algorithm for sparse instances. Our distributed memory auction algorithms are fully memory scalable. This research has been supported by IGA CTU under grant CTU0308013 and under research program MSMT 6840770014.     

Dual multiplicative algorithms for an entropy-linear programming problem

A multiplicative-barrier generalization of the Cauchy gradient descent method is proposed and studied. The technique is used to search for dual variables in the entropy maximization problem with affine constraints, which arises, for example, in the simulation of equilibria in macroscopic systems. For this class of problems, the dual variables can be used to effectively determine the primal ones. The global convergence of the iterative algorithms proposed is proved.     

无穷维Hamilton算子的二次数值域

研究了一类无界无穷维Hamilton算子的二次数值域的性质,进而,应用二次数值域来刻画了无穷维Hamilton算子谱的分布范围,并给出了二次数值域的闭包包含谱集的结论.     

高振动积分的一种新的有效Levin-type配置法

本文给出了高振动积分的一种新的有效Levin-type配置法, 并对它的有效性和精度进行了检验, 与Levin配置法相比较, 这种方法具有更高的精度而且容易实现.     

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.     

On the characterization of strong convergence of an iterative algorithm for a class of multi-valued variational inclusions

This paper introduces an Ishikawa type iterative algorithm for finding approximating solutions of a class of multi-valued variational inclusion problems. Characterization of strong convergence of this iterative method is established. L. C. Ceng’s research partially supported by the Teaching and Research Award Fund for Outstanding Young Teachers in Higher Education Institutions of MOE, China and the Dawn Program Foundation in Shanghai. S. Schaible’s research partially supported by the National Science Council of Taiwan. This research was partially supported by the grant NSC 96-2628-E-110-014-MY3.