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Mapping without the sun
Zhang, Jixian

Gram-Schmidt Orthonormalization Method:
Since orthonormal vectors are easier to work with, we need an
algorithm to change vectors aj,..., a k by linear combinations to
an orthonormal set. The Gram-Schmidt method produces
orthonormal vectors q k ... q k such that the span of q,... q, is the
same as the span of a,; : : : ; aj for 1 ,j, k. We do this by
iteratively subtracting o_ projections onto previous subspaces:
a’j = aj - (q T i aj)qi -...-(q^-^jjqj-l; where a’j is subsequently
discarded if it is 0, or normalized if it is not:
qj =a Vll a ’jll
If a b ...a k are linearly independent, we do not need to worry
about discarding any vectors, they will be linearly independent
and thus, a fortiori, nonzero.
We usually need to use QR factorisations to find the best
linear least squares solution to some data. Thus in order to
solve the (approximate) equations Ax approx= b, for x where A
is an m x n matrix (m>n) we really need to solve the
optimisation problem
2.3 sigular value decomposition, SVD
Singular value decomposition takes a rectangular matrix of
gene expression data (defined as A, where A is a n xp matrix)
in which the n rows represents the genes, and the p columns
represents the experimental conditions. The SVD theorem
y^nxp_ jjnxn gnxp yT pxp
Where U T U = I nxp V T V = I pxp (i.e. U and V are orthogonal)
Where the columns of U are the left singular vectors [gene
coefficient vectors)-, S (the same dimensions as A) has singular
values and is diagonal (mode amplitudes)-, and V T has rows that
are the right singular vectors (expression level vectors). The
SVD represents an expansion of the original data in a
coordinate system where the covariance matrix is diagonal.
Every matrix has a singular value decomposition .SVD is a
kind of reliable but may cost ten more time than QR
decomposition. Calculating the SVD consists of finding the
eigenvalues and eigenvectors of AA T and A T A. The eigenvectors
of A T A make up the columns of V, the eigenvectors of AA T make
up the columns of U. Also, the singular values in S are square
roots of eigenvalues from AA T or A T A. The singular values are the
diagonal entries of the S matrix and are arranged in descending
order. The singular values are always real numbers. If the matrix
A is a real matrix, then U and V are also real.
SVD factorizations are usually used to find the best linear least
squares solution and compression to some data, that digital image
is transformed into singular value matrix that contains non-zero
singular values by singular value decomposition (SVD), the
image is compressed.
A matrix library is for performing matrix algebra calculations
in programs in easy and efficient manner for engineering &
scientific works. They ofen supports most of the matrix algebra
operations and enables programmers to use matrix object just
like other built-in data types in their programs. It supports
arithmetic operations, sub-matrix operations, inversion, various
matrix decompositions, solution of simultaneous linear
equations, eigen value and eigen vector problems, and much
3.1 meschach:
Library for performing operations on matrices and vectors
Meschach is a library of routines written in C for matrix
computations. These include operations for basic numerical
linear algebra; routines for matrix factorisations; solving
systems of equations; solving least squares problems;
computing eigenvalues, eigenvectors and singular
values;sparse matrix computations including both direct and
iterative methods. This package makes use of the features of
the C programming language: data structures, dynamic
memory allocation and deallocation, pointers, functions as
parameters and objects. Meschach has a number of self-
contained data structures for matrices, vectors and other
mathematical objects.
Meschach has the virtue of compiling under Linux and most
other operating systems, and is openly available under
copyright, provided the customary acknowledgment is
observed and errors are reported. Meschach was designed to
solve systems of dense or sparse linear equations, compute
eigenvalues and eigenvectors, and solve least squares problems,
among other things. It provides nearly 400 functions for
doubles and complex numbers. Matrices can easily be sent to
files or to the standard output. Meschach computes Fast Fourier
Transforms, extracts columns and rows, and computes
eigenvalues of symmetric matrices. You can fill a matrix with
random ints and complexes. Meschach code is easily extensible,
if you happen to be doing computational work in C that needs
matrices, this is a highly useful library.
3.2 The CwMtx library for matrix, vector and quaternion
CwMtx is a library wrote in C++ that provides the matrix and
vector operations that are used extensively in engineering and
science problems. A special feature of this library is the
quaternion class which implements quaternion math.
Quaternions are very useful for attitude determination in 3D
space because they do not suffer from singularities.
Furthermore, successive rotations and transformations of
vectors can be accomplished by simple quaternion
multiplication. Attitude dynamics can be expressed in a very
compact form using quaternions.
3.3 Blitz
Blitz ++ is a C++ class library for scientific computing
which provides performance on par with Fortran 77/90. It uses
template techniques to achieve high performance. The current
versions provide dense arrays and vectors, random number
generators, and small vectors and matrices. Blitz++ is
distributed freely under an open source license, and
contributions to the library are welcomed. It is distributed
under the GNU GPL, and with it you can freely create objects.
It supports the KAI, Intel, gcc, Metroworks, and Cray
C++ compilers and provides an n-dimensional array class that
can contain integral, floating, complex, and well-behaved user-
defined types. Its constructor is more complex that the CwMtx
The technique to solve sparse matrix including direct Factor
and iterative method. UMFPACK, superLU, intel
PARDISO,GSS are all famous direct solvers for sparse