Skeleton is a frequently applied shape feature to represent the general form of an object. Thinning is an iterative object reduction technique for producing a reasonable approximation to the skeleton in a topology preserving way. This paper describes a sequential 3D thinning algorithm for extracting medial lines of objects in (26, 6) pictures. Our algorithm has been successfully applied in medi...

In high-dimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object q can be a very expensive task, because of the poor partitioning operated by index structures – the so-called “curse of dimensionality”. This also affects approximately correct (AC) algorithms, which return as result a point whose distance from q is less than (1 + ) times the distance between ...

A technique for maintaining the positive definiteness of the matrices in the quasi-Newton version of the SQP algorithm is proposed. In our algorithm, matrices approximating the Hessian of the augmented Lagrangian are updated. The positive definiteness of these matrices in the space tangent to the constraint manifold is ensured by a so-called piecewise line-search technique, while their positive...

The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the...

In this paper, by combining the splitting method of augmented Lagrange function (ALF) with sequential quadratic programming (SQP) approximation, a novel ALF-based algorithm SQP structure is proposed for multi-block linear constrained nonconvex separable optimization. The new uses idea to decompose original problem into several small-scale subproblems. Meanwhile, approximation and Armijo-type li...

We present a new approach to using neural networks approximate variational equations, based on the adaptive construction of sequence finite-dimensional subspaces whose basis functions are realizations networks. The can be used define standard Galerkin approximation equation. This enjoys advantages including following: sequential nature algorithm offers systematic enhancing accuracy given approx...

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximat...

We design and implement an efficient parallel approximation algorithm for the problem of maximum weight perfect matching in bipartite graphs, i.e. the problem of finding a set of non-adjacent edges that covers all vertices and has maximum weight. This problem differs from the maximum weight matching problem, for which scalable approximation algorithms are known. It is primarily motivated by fin...

In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters. We also propose a sequential Monte Carlo approximation of our online EM algorithm. We show the performance of the proposed method ...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...