結合基因法則之類神經網路技術在手寫辨識系統之應用

Then, it is easy to see that there are 9 problems for which the iterative numbers of the algorithm using ψ α,θ,p in the case of θ = 1 and p = 3 are less than the one of the

Section 3 is devoted to developing proximal point method to solve the monotone second-order cone complementarity problem with a practical approximation criterion based on a new

Although we have obtained the global and superlinear convergence properties of Algorithm 3.1 under mild conditions, this does not mean that Algorithm 3.1 is practi- cally efficient,

In this chapter we develop the Lanczos method, a technique that is applicable to large sparse, symmetric eigenproblems.. The method involves tridiagonalizing the given

Like the proximal point algorithm using D-function [5, 8], we under some mild assumptions es- tablish the global convergence of the algorithm expressed in terms of function values,

• When this happens, the option price corresponding to the maximum or minimum variance will be used during backward induction... Numerical

• When this happens, the option price corresponding to the maximum or minimum variance will be used during backward induction... Numerical

We will design a simple and effective iterative algorithm to implement “The parallel Tower of Hanoi problem”, and prove its moving is an optimal