one of the problems that sometimes occur in gas allocation optimization is instability phenomenon. this phenomenon reduces the oil production and damages downhole and surface facilities. different works have studied the stability and suggested some solutions to override it, but most of them (such as making the well intelligent) are very expensive and thus they are not applicable to many cases. ...

Here, a quasi-Newton algorithm for constrained multiobjective optimization is proposed. Under suitable assumptions, global convergence of the algorithm is established.

‎artificial neural networks have the advantages such as learning, ‎adaptation‎, ‎fault-tolerance‎, ‎parallelism and generalization‎. ‎this ‎paper is a scrutiny on the application of diverse learning methods‎ ‎in speed of convergence in neural networks‎. ‎for this aim‎, ‎first we ‎introduce a perceptron method based on artificial neural networks‎ ‎which has been applied for solving a non-singula...

A quasi-Newton algorithm for semi-infinite programming using an Leo exact penalty function is described, and numerical results are presented. Comparisons with three Newton algorithms and one other quasi-Newton algorithm show that the algorithm is very promising in practice. AMS classifications: 65K05,90C30.

in this paper, optimal distributed control of the time-dependent navier-stokes equations is considered. the control problem involves the minimization of a measure of the distance between the velocity field and a given target velocity field. a mixed numerical method involving a quasi-newton algorithm, a novel calculation of the gradients and an inhomogeneous navier-stokes solver, to find the opt...

An expectation maximization (EM) algorithm is derived to estimate the parameters of a phylogenetic model, a probabilistic model of molecular evolution that considers the phylogeny, or evolutionary tree, by which a set of present-day organisms are related. The EM algorithm is then extended for use with a combined phylogenetic and hidden Markov model. An efficient method is also shown for computi...

In this paper, we derive and discuss a new adaptive quasi-Newton eigen-estimation algorithm and compare it with the RLS-type adaptive algorithms and the quasi-Newton algorithm proposed by Mathew et al. through experiments with stationary and nonstationary data.

This paper examines the e ectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback/feedforward structure of the recur...