The problem of minimizing an objective that can be written as the sum of a set of n smooth and strongly convex functions is challenging because the cost of evaluating the function and its derivatives is proportional to the number of elements in the sum. The Incremental Quasi-Newton (IQN) method proposed here belongs to the family of stochastic and incremental methods that have a cost per iterat...

This paper presents a multicriterion algorithm for dealing with joint facility location and network design problems, formulated as bi-objective problems. The algorithm is composed of two modules: a multiobjective quasi-Newton algorithm, that is used to find the location of the facilities; and a multiobjective genetic algorithm, which is responsible for finding the efficient topologies. These mo...

In this paper, the algorithm for large-scale nonlinear equations is designed by the following steps: (i) a conjugate gradient (CG) algorithm is designed as a sub-algorithm to obtain the initial points of the main algorithm, where the sub-algorithm's initial point does not have any restrictions; (ii) a quasi-Newton algorithm with the initial points given by sub-algorithm is defined as main algor...

We first introduce a constrained minimization formulation for the generalized symmetric eigenvalue problem and then recast it into an unconstrained minimization problem by constructing an appropriate cost function. Minimizer of this cost function corresponds to the eigenvector corresponding to the minimum eigenvalue of the given symmetric matrix pencil and all minimizers are global minimizers. ...

Breast cancer diagnosis has been approached by various machine learning techniques for many years. This paper presents a study on classification of Breast cancer using Feed Forward Artificial Neural Networks. Back propagation algorithm is used to train this network. The performance of the network is evaluated using Wisconsin breast cancer data set for various training algorithms. The highest ac...

We propose a generic approach to accelerate gradient-based optimization algorithms with quasiNewton principles. The proposed scheme, called QuickeNing, can be applied to incremental first-order methods such as stochastic variance-reduced gradient (SVRG) or incremental surrogate optimization (MISO). It is also compatible with composite objectives, meaning that it has the ability to provide exact...

Recurrent Neural Networks (RNNs) are powerful models that achieve unparalleled performance on several pattern recognition problems. However, training of RNNs is a computationally difficult task owing to the well-known “vanishing/exploding” gradient problems. In recent years, several algorithms have been proposed for training RNNs. These algorithms either: exploit no (or limited) curvature infor...

The analysis of lifetime data is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semi-parametric models are the proportional hazards model and the accelerated failure time (AFT) model. The proportional hazards model is computationally advantageous over virtually any other competing semi-parametric model because the ubiquito...

A new and highfy efficient algorithm for nonlinear minimax optimization is presented. The algorithm, based on the work of Hald and Madsen, combines linear programming methods with quasi-Newton methods and has sure convergence properties. A critical review of the existing minimax algorithms is given, and the relation of the qnasi-Newton iteration of the algorithm to the Powell method for rstssdi...