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For example, matrices of size (mxn) will be generated if row_numbers[i] == m and col_numbers[i] == n for some i.
The identification of the solution involves matrices of size (3 × 3) computed off-line.
The explicit polynomial that we consider is the iterated matrix multiplication polynomial of n generic matrices of size n×n.
Furthering these developments, we present GPU design and optimization techniques for high-performance batched one-sided factorizations of millions of tiny matrices (of size 32 and less).
This method is based on a Hadamard matrix of size N×N and a pair of Hadamard matrices of size M×M.
We show experimental results on matrices of size up to the order of one billion with nearly perfect scaling by using up to 1024 MPI processes.
On a K40c GPU for contractions resulting in GEMMs on square matrices of size 8 for example, we are 2.8× faster than CUBLAS, and 8.5× faster than MKL on 16 cores of Intel Xeon E5-2670 (Sandy Bridge) 2.60GHz CPUs.
As an application of these results, we derive a hierarchy of convex LMI inner approximations (affine sections of the cone of positive definite matrices of size m) of the nonconvex set of Schur stable polynomials of given degree n
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In the parity-check matrix H, there are eight sub-matrices of size (frac {N}{4} times frac {N}{4}).
When | I| = 2| J| = r (lines 10-15 in Procedure Compute-Inside-Sub-Matrix, Table 2), the algorithm performs two recursive calls with sub-matrices of size r 2 × r 2, a matrix multiplication between two r 2 × r 2 matrices, and a matrix addition of two r 2 × r 2 matrices.
When | I| = | J| = r (lines 4-9 in Procedure Compute-Inside-Sub-Matrix, Table 2), the algorithm performs two recursive calls with sub-matrices of size r × r 2, a matrix multiplication between an r × r 2 and an r 2 × r 2 matrices, and a matrix addition of two r × r 2 matrices.
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