论文信息 - Large-Scale Unconstrained Optimization On the Fps 164 and Cray X-MP Vector Processors

Large-Scale Unconstrained Optimization On the Fps 164 and Cray X-MP Vector Processors

Among methods for large-scale unconstrained optimiza tion, partitioned quasi-Newton methods seem fairly suit able for vector computing. This paper reports on efforts to adapt a method of this type to vector processors. The conclusion is that a satisfying improvement in perfor mance can be obtained, especially when the inner loops deal with the elements of the objective function. Perfor mances on FPS 164 and CRAY X-MP machines are compared.

M. Lescrenier | Ph. L. Tointt | M. Lescrenier

[1] F. Gustavson,et al. Implementing Linear Algebra Algorithms for Dense Matrices on a Vector Pipeline Machine , 1984 .

[2] Iain S. Duff,et al. The Use of Vector and Parallel Computers in the Solution of Large Sparse Linear Equations , 1987 .

[3] John E. Dennis,et al. Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[4] P. Toint,et al. Partitioned variable metric updates for large structured optimization problems , 1982 .

[5] Roger W. Hockney,et al. (r∞, n1/2, s1/2) measurements on the 2-CPU CRAY X-MP , 1985, Parallel Comput..

[6] T. Steihaug. The Conjugate Gradient Method and Trust Regions in Large Scale Optimization , 1983 .

[7] Iain S. Duff. The use of supercomputers in Europe , 1985 .

[8] Andreas Griewank,et al. Numerical experiments with partially separable optimization problems , 1984 .

[9] Josef Stoer,et al. Solution of Large Linear Systems of Equations by Conjugate Gradient Type Methods , 1982, ISMP.