Technical Report 2016-2, Wake Forest University, Department of Mathematics, 2016. Numerical results suggest that the SC-SR1 method is able to solve trust-region subproblems to high accuracy even in the so-called “hard case”. In the other proposed norm, one of the resulting subprob- lems has a closed-form solution while the other is easily solvable using techniques that exploit the structure of L-SR1 matrices. Reshape the matrix into a four dimensional matrix (dimensions x1,x2,x3,x4), where each submatrix is located in the x1-x3 plane. Reshaping a vector into a larger matrix with arbitrary m and n. The reshape(A,2,5,) command reshapes your A matrix into a three-dimensional tensor of dimension 2 x 5 x nblocks, where nblocks is the number of blocks in A. In R, function vec() of package ks allows vectorization and function vech. From vector to matrix reshape every ith rows for each column. In Matlab/GNU Octave a matrix A can be vectorized by A(:). Using one of the proposed shape-changing norms, the resulting subproblems then have closed-form solutions. Asking for help, clarification, or responding to other answers. The method takes advantage of two shape-changing norms to decompose the trust-region subproblem into two separate problems. For example, reshape a 3-by-4 matrix to a 2-by-6 matrix. We present a MATLAB implementation of the shape-changing sym- metric rank-one (SC-SR1) method that solves trust-region subproblems when a limited-memory symmetric rank-one (L-SR1) matrix is used in place of the true Hessian matrix. reshape(A, Sizes) reshape(A, s1, s2) reshape(A, s1, ) reshape(A, s1, s2, s3. The reshape function changes the size and shape of an array.
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