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BLinAlg-FFT

Version: 4.3 By: Oleg Kiselyov E-mail: oleg@pobox.com Web page: Available Release Date: Available Now	Info Last Modified: 2/16/99 Requires BeOS version: R4 PPC, R4 Intel License: Freeware Cost: Source Available?: Yes
R4-PPC Download (213 kb)	R4-Intel Download (213 kb)

This classlib declares Matrix, Vector, subMatrices, and LAStreams over real 
domain; contains efficient and fool-proof  implementations of level 1 and 2 
BLAS (element-wise operations and various multiplications), transposition, 
determinant evaluation and matrix inverse. There are operations on a single row
/col/diagonal of a matrix. Distinct features of the package are Matrix 
views, various Matrix streams, and LazyMatrices. Lazy construction allows one 
to write matrix  expressions in a natural way while imposing no hidden 
temporaries, no deep copying, and no reference counting. 

LinAlg stresses Matrix streams, which provide a sequential view/access to a 
matrix or its parts. LABlockStreams may span over an arbitrary rectangular 
block of a matrix, including the whole matrix, a single matrix element, and all 
other block sizes in between. Matrix streams are seek-able and subrange-able. A 
stream or a substream are always created in-line; they do not allocate any heap 
storage, and are safe. 

The package proves that many of Linear Algebra algorithms indeed require only 
sequential access to a matrix or its subblocks, which is simpler and faster 
than a random access. Notable examples of such "serial algorithms" are matrix 
inverse and Householder bidiagonalizations. Every feature of the package is 
extensively tested by validation code (included). 

Major attractions of the current complete release (v4.3) are 

- Hooke-Jeeves multidimensional optimization of functors 
- Aitken-Lagrange interpolation 
- Singular Value Decomposition and its application to solving Ax=b, where 
matrix A does not have to be square, and b does not have to be a vector. 
- More support for Matrix and Vector promises:
	Matrix A = inverse(D); C = A*B;
  do what they say, as efficiently as possible. 
- A great variety of matrix streams, e.g.:
	assert(
нн	of_every(ConstMatrixRow(m,i)).max_abs(of_every(vect))
нн		== 2.0 ); 

A README file tells much more about the features and how to use them. It 
contains many commented code snippets. NumMath.dr file tells what the other 
files are for. In addition, the archive contains an already built library -- 
libla.a, BeOS PPC R4b4. But the source code is cross-platform compatible and 
compiles as it is on various flavors of UNIX, Linux, and WinNT. See the README 
file for more details

Mirror Download Sites:

Mirror Name			R4-PPC	R4-Intel
Be Europe Mirror			PPC	Intel
Austria Mirror			PPC	Intel
Australia Mirror			PPC	Intel
Germany Mirror (UUNet)			PPC	Intel
Japan Mirror #1			PPC	Intel
Japan Mirror #2 (Nagoya)			PPC	Intel
UK Mirror			PPC	Intel
US Mirror #1 (UIUC)			PPC	Intel
US Mirror #2 (cdrom.com)			PPC	Intel