The purpose of this work is to analyze the performance of back-propagation feed-forward algorithm using various different activation functions for the neurons of hidden and output layer and varying the number of neurons in the hidden layer. For sample creation, 250 numerals were gathered form 35 people of different ages including male and female. After binarization, these numerals were clubbed ...

The task to compute 3D reconstructions from large amounts of data has become an active field of research within the last years. Based on an initial estimate provided by structure from motion, bundle adjustment seeks to find a solution that is optimal for all cameras and 3D points. The corresponding nonlinear optimization problem is usually solved by the Levenberg-Marquardt algorithm combined wi...

We study the Riemannian optimization methods on the embedded manifold of low rank matrices for the problem of matrix completion, which is about recovering a low rank matrix from its partial entries. Assume m entries of an n× n rank r matrix are sampled independently and uniformly with replacement. We first prove that with high probability the Riemannian gradient descent and conjugate gradient d...

In this paper, we consider the method for solving finite minimax problems. By using exponential penalty function to smooth problems, a new three-term nonlinear conjugate gradient is proposed which generates sufficient descent direction at each iteration. Under standard assumptions, global convergence of with Armijo-type line search established. Numerical results are given illustrate that can ef...

We propose a class of very simple modifications gradient descent and stochastic leveraging Laplacian smoothing. show that when applied to large variety machine learning problems, ranging from logistic regression deep neural nets, the proposed surrogates can dramatically reduce variance, allow take larger step size, improve generalization accuracy. The methods only involve multiplying usual (sto...

Nonlinear conjugate gradient method is well known in solving large-scale unconstrained optimization problems due to it’s low storage requirement and simple to implement. Research activities on it’s application to handle higher dimensional systems of nonlinear equations are just beginning. This paper presents a Threeterm Conjugate Gradient algorithm for solving Large-Scale systems of nonlinear e...

Reconstruction of target images from phase-only hologram (POH) has the advantages high diffraction efficiency and no conjugate terms. The Gerchberg-Saxton (GS) algorithm is a classical applied to recover phase, but it most likely stagnantes after few iterations. This paper proposes hybrid iterative Amplitude Weighting Phase Gradient Descent (AW-PGD) generate higher-quality POH. Firstly, quadrat...

It is well known that conjugate gradient methods are useful for solving large-scale unconstrained nonlinear optimization problems. In this paper, we consider combining the best features of two methods. particular, give a new method, based on hybridization DY (Dai-Yuan), and HZ (Hager-Zhang) The hybrid parameters chosen such proposed method satisfies conjugacy sufficient descent conditions. show...