Download e-book for iPad: Computing for Numerical Methods Using Visual C++ by Shaharuddin Salleh, Visit Amazon's Albert Y. Zomaya Page,

By Shaharuddin Salleh, Visit Amazon's Albert Y. Zomaya Page, search results, Learn about Author Central, Albert Y. Zomaya, , Sakhinah A. Bakar

ISBN-10: 0470127953

ISBN-13: 9780470127957

Steered for all Engineers, very functional booklet on Numerical Computing with Examples in visible C++ utilizing MFC. The hyperlink of the code obtain is damaged, whereas studying eventually obtained it - this is the single. ftp://ftp.wiley.com/public/sci_tech_med/computing_numerical/ . get pleasure from :)

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Extra info for Computing for Numerical Methods Using Visual C++

Example text

The basic rule allowing two matrices to be multiplied is the number of columns in the first matrix must be equal to the number of rows of the second matrix, which is k = 1, 2, . . , P. The product of A and B is then matrix C = [ci j ], or AB = C. For example, if A and B are matrices of size 3 × 4 and 4 × 2, respectively, then their product is matrix C with the size of 3 × 2, as follows:  c11  C = c21 c31 c12   c22  c32  a11  = a21 a12 a13 a22 a23 a31 a32 a33   b11 a14   b21 a24   b31 a34 b41 b12  b22    b32  b42  a11 b11 + a12 b21 + a13 b31 + a14 b41  = a21 b11 + a22 b21 + a23 b31 + a24 b41 a31 b11 + a32 b21 + a33 b31 + a34 b41 a11 b12 + a12 b22 + a13 b32 + a14 b42   a21 b12 + a22 b22 + a23 b32 + a24 b42  .

Similarly, MatInverse(Q,B) receives data for matrix B, computes its inverse of B, and stores the result into a new matrix Q. The function MatAdd(1,V,U,S) adds the matrices U and S and then stores the result into V. The Boolean parameter 1 here means addition, or V=U+S; with 0 the function performs subtraction, or V=U-S. cpp: computing Z = A2 B −1 + A−1 B − AB. 5 ALGEBRA OF COMPLEX NUMBERS Not all numbers in existence are real. There are also cases in which a number exists from an imagination based on some undefined entity.

The elements in matrix C become   4 4  c11  C = c21 c31 a1k bk1   k=1 c12    4 c22  =   a2k bk1 k=1 c32  4  a3k bk1 k=1 a1k bk2     a2k bk2  . 4) k=1 for i = 1, 2, . . , M, and j = 1, 2, . . , N . This compact form is also the algorithmic solution for the matrix multiplication problem as program codes can easily be designed from the solution. We discuss the program design for the above problem. Three loops with the iterators i, j, and k are required. As k is the common variable representing both the row number of A and the column number of B, it is ideally placed in the innermost loop.

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Computing for Numerical Methods Using Visual C++ by Shaharuddin Salleh, Visit Amazon's Albert Y. Zomaya Page, search results, Learn about Author Central, Albert Y. Zomaya, , Sakhinah A. Bakar


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