In this paper, we solve a problem describing the mechanical deformations of a porous medium in the presence of a monophasic linear flow or a two phase nonlinear flow with the purpose of modelizing subsidence of hydrocarbon reservoirs. An essential characteristics of this problem is that the mechanical deformation and the flow have different time scales. In petroleum industry, one uses different...

In this paper we propose a new Riemannian conjugate gradient method for optimization on the Stiefel manifold. We introduce two novel vector transports associated with the retraction constructed by the Cayley transform. Both of them satisfy the Ring-Wirth nonexpansive condition, which is fundamental for convergence analysis of Riemannian conjugate gradient methods, and one of them is also isomet...

We describe parallelization for distributed memory computers of a preconditioned Conjugate Gradient method, applied to solve systems of equations emerging from Elastic Light Scattering simulations. The execution time of the Conjugate Gradient method is analyzed theoretically. First expressions for the execution time for three different data decompositions are derived. Next two processor network...

In this paper, we present a new subspace adaptive algorithm for the blind separation problem of a convolutivemixture. The major advantage of such an algorithm is that almost all the unknown parameters of the inverse channel can be estimated using only second-order statistics. In fact, a subspace approach was used to transform the convolutive mixture into an instantaneous mixture using a criteri...

Recently, important contributions on convergence studies of conjugate gradient methods have been made by Gilbert and Nocedal [6]. They introduce a “sufficient descent condition” to establish global convergence results, whereas this condition is not needed in the convergence analyses of Newton and quasi-Newton methods, [6] hints that the sufficient descent condition, which was enforced by their ...

A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural network but requires only O(N) memory usage, where N is the number of weights in the network. The perf...

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good prop...

Multiple users communicate with individually assigned codes on a single radio carrier frequency in a DS-CDMA system. Mutual interference among codes limits the capacity of the system. Conventional detectors of receivers are for single-user, which are designed neglecting the mutual interference. Multi-user detection is expected to enhance the capacity. In this paper, a new scheme is proposed cor...

In this paper, the Artificial Neural Network (ANN) approach is applied for forecasting groundwater level fluctuation in Aghili plain,southwest Iran. An optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (GDM), levenberg marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG). Rain,evaporation, relative...

This paper proposes an enhancement of the non linear conjugate gradient algorithm for some non-smooth problems. We first extend some results of descent algorithms in the smooth case for convex non-smooth functions. We then construct a conjugate descent algorithm based on the proximity operator to obtain a descent direction. We finally provide a convergence analysis of this algorithm, even when ...