三維網格參數化及其應用之研究(I)

In this paper, we have studied a neural network approach for solving general nonlinear convex programs with second-order cone constraints.. The proposed neural network is based on

Then, we tested the influence of θ for the rate of convergence of Algorithm 4.1, by using this algorithm with α = 15 and four different θ to solve a test ex- ample generated as

In this paper, we extended the entropy-like proximal algo- rithm proposed by Eggermont [12] for convex programming subject to nonnegative constraints and proposed a class of

In this paper, we have shown that how to construct complementarity functions for the circular cone complementarity problem, and have proposed four classes of merit func- tions for

For the proposed algorithm, we establish its convergence properties, and also present a dual application to the SCLP, leading to an exponential multiplier method which is shown

We have also discussed the quadratic Jacobi–Davidson method combined with a nonequivalence deflation technique for slightly damped gyroscopic systems based on a computation of

(It is also acceptable to have either just an image region or just a text region.) The layout and ordering of the slides is specified in a language called SMIL.. SMIL is covered in

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