svd code in c

This is my new post about how to create svd code in c with LAPACK routines. I get a problem in programming about gridding and I want to use svd code in c (I create svd code with LAPACK routines and call that from C) to solve this problem computation. In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics (from wikipedia). We can use svd in Matlab with command [u, s, v] = svd(a). When you read in Matlab documentation, Matlab svd code uses the LAPACK routines to compute the svd (singular value decomposition). From this post, I try to create a simple svd code to solve svd computation with LAPACK routines. In my code, I use the last post about create 2d Array in C.

Singular value decomposition takes a rectangular matrix of gene expression data (defined as A, where A is a n x p matrix) in which the n rows represents the genes, and the p columns represents the experimental conditions. The SVD theorem states:

A = U*S*VT

Result from this svd code computation is three matrix/array :

  • U (Where the columns of U are the left singular vectors (gene coefficient vectors))
  • S (S (the same dimensions as A) has singular values and is diagonal (mode amplitudes))
  • VT has rows that are the right singular vectors (expression level vectors)

This is my simple svd code in c with LAPACK routine, you can save this code with name svdCLapack.c

You can compile this svd code in c (Singular Value Decomposition code) with command :

gcc svdCLapack.c -lm -llapack -o svd

Maybe you want to ask, why output from this svd LAPACK is different with Matlab. I dont know why this happen. But I think Matlab modified this LAPACK code :). But if you want to check output from this svd LAPACK code is right or wrong, You can compute output from U, S, and V. From above equation, A=U*S*VT . So, You can compute output from this svd code in c and I get this output from this svd code in c is right.


2 thoughts on “svd code in c


    “Advanced Eigenvalue Vector Decomposition”

    We have made a significant improvement to the Singular Value
    Decomposition methodology, This is actually an understatement.

    We have discovered that the current Singular Value Decomposition mathematical techniques and resulting FORTRAN and C code is quite slow and inaccurate and what we have accomplished is to speed up computer execution by more than a factor of 1000 and to improve numerical accuracy by about 12 bits out of 48 bits for the standard double mantissa That is to say that there is no more than 1 bit different between the exact answer and my new solution method .Previous or current methods can barely achieve 24 bit accuracy (out of48).This improvement will make it possible to recognize red, green , blue, black, grey and white infrared images in real time as the first application .

    The range of applications for this new technology can go well
    beyond this.

    Of course, we expect skeptics about these claims, but we have demo
    programs that will read your data and produce results that they can compare and we can even executed this new code on their computers if they desire.

    How AEVD Improves SVD Performance

    AEVD Technology, LLC offers a fully developed, advanced form of the Singular Value Decomposition (SVD) algorithm, which offers a generational advance in speed and accuracy. Our Advanced Eigenvalue Vector Decomposition (or AEVD) was built upon a recognition of the shortcomings in how computers manipulate numbers, data and calculations, and reflects a painstaking analysis of the incremental steps in SVD processing to provide a faster process with fewer steps and improved inaccuracy.

    The SVD mathematical proposition is linearly independent, in an algebraic sense, of the similarity theorem and as such provides a variety of available solution paths. One such path is to first reduce the input array to a real bidiagonal matrix with a sequence of intermediate left and right unitary transformations. This reduction to a real bidiagonal matrix is usually chosen to be a real diagonal and having one real super diagonal. All of the remaining matrix elements are numerically considered as zero. It is possible to choose other reduced forms of the input matrix, but the use of a real bidiagonal array provides for the most numerically accurate and computationally rapid solution. Additional numerical stability and computer accuracy is obtained by AEVD by choosing unitary transformations that place the larger bidiagonal elements in the upper left and the smaller elements in the lower right positions. This is true since the final determination of the left and right transformations and the SVD weights are always an iterative process for matrix sizes greater than four. Even for matrix sizes of four and less iterative methods are usually simpler and require computational steps comparable to closed form algebraic solutions. Also, when a real bidiagonal array format is chosen as the final iterate, the left and right transformations employed during iteration are orthogonal matrices. Other SVD iterative solution methods available employ orthogonal variations such as Jacobi rotation, Givens, and Householder reduction algorithms. Consistent among the more efficient and accurate SVD algorithms is the choice of the real

    John Rawlins


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