CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method
نویسندگان
چکیده
منابع مشابه
On the Steepest Descent Method for Matrix
We consider the special case of the restarted Arnoldi method for approximating the product of a function of a Hermitian matrix with a vector which results when the restart length is set to one. When applied to the solution of a linear system of equations, this approach coincides with the method of steepest descent. We show that the method is equivalent with an interpolation process in which the...
متن کاملOn the Steepest Descent Method for Matrix
We consider the special case of the restarted Arnoldi method for approximating the product of a function of a Hermitian matrix with a vector which results when the restart length is set to one. When applied to the solution of a linear system of equations, this approach coincides with the method of steepest descent. We show that the method is equivalent with an interpolation process in which the...
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The possibilities inherent in steepest descent methods have been considerably amplified by the introduction of the Barzilai-Borwein choice of step-size, and other related ideas. These methods have proved to be competitive with conjugate gradient methods for the minimization of large dimension unconstrained minimization problems. This paper suggests a method which is able to take advantage of th...
متن کاملSteepest Descent
The steepest descent method has a rich history and is one of the simplest and best known methods for minimizing a function. While the method is not commonly used in practice due to its slow convergence rate, understanding the convergence properties of this method can lead to a better understanding of many of the more sophisticated optimization methods. Here, we give a short introduction and dis...
متن کاملSteepest descent method on a Riemannian manifold: the convex case
In this paper we are interested in the asymptotic behavior of the trajectories of the famous steepest descent evolution equation on Riemannian manifolds. It writes ẋ (t) + gradφ (x (t)) = 0. It is shown how the convexity of the objective function φ helps in establishing the convergence as time goes to infinity of the trajectories towards points that minimize φ. Some numerical illustrations are ...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2011
ISSN: 1471-2105
DOI: 10.1186/1471-2105-12-164