input matrixの例文

• During the execution of Bareiss algorithm, every integer that is computed is the determinant of a submatrix of the input matrix.
• Equivalently, R can also be viewed as a submatrix of the input matrix A such that R = M \ times N where M \ subseteq X and N \ subseteq Y.
• Did you try disproving the claim by computing the two for some small randomly chosen symmetric input matrix " R " ?-- Lambiam 17 : 42, 6 August 2007 ( UTC)
• Cannon's algorithm, also known as the " 2D algorithm ", partitions each input matrix into a block matrix whose elements are submatrices of size } } by } }, where is the size of fast memory.
• We can now reconstruct a document ( column vector ) from our input matrix by a linear combination of our features ( column vectors in ) where each feature is weighted by the feature's cell value from the document's column in.
• Using the above definition, it is useful to think of the function as a matrix ( called the " input matrix " ) where each row of the matrix corresponds to x \ in X and each column corresponds to y \ in Y.
• Cannon's algorithm, also known as the " 2D algorithm ", is a communication-avoiding algorithm that partitions each input matrix into a block matrix whose elements are submatrices of size } } by } }, where is the size of fast memory.
• As a consequence of this algorithm, the fill-in ( the set of nonzero matrix entries created in the Cholesky decomposition that are not part of the input matrix structure ) is limited to at most the square of the separator size at each level of the recursive partition.
• Specifically, actuator faults are represented by the new input matrix \ mathbf { B } _ f, sensor faults are represented by the output map \ mathbf { C } _ f, and internal plant faults are represented by the system matrix \ mathbf { A } _ f.
• In 2001, Ernesto Kofman proved a remarkable property of the quantized-state system simulation method : namely, that when the technique is used to solve a input matrix B, it was shown in [ CK06 ] that the absolute error vector \ vec { e } ( t ) is bounded above by
• Considering a 0 / 1 input matrix M _ f = [ f ( x, y ) ] _ { x, y \ in \ { 0, 1 \ } ^ n }, the minimum number of bits exchanged to compute f deterministically in the worst case, D ( f ), is known to be bounded from below by the logarithm of the rank of the matrix M _ f.