77 lines
		
	
	
		
			4.8 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			77 lines
		
	
	
		
			4.8 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
    LA: linear algebra C++ interface library
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    Copyright (C) 2008- Jiri Pittner <jiri.pittner@jh-inst.cas.cz> or <jiri@pittnerovi.com>
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    complex versions written by Roman Curik <roman.curik@jh-inst.cas.cz>
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    cuda interface contributed by Miroslav Sulc <miroslav.sulc@jh-inst.cas.cz>
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    This program is free software: you can redistribute it and/or modify
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    it under the terms of the GNU General Public License as published by
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    the Free Software Foundation, either version 3 of the License, or
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    (at your option) any later version.
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    This program is distributed in the hope that it will be useful,
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    but WITHOUT ANY WARRANTY; without even the implied warranty of
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    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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    GNU General Public License for more details.
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    You should have received a copy of the GNU General Public License
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    along with this program.  If not, see <http://www.gnu.org/licenses/>.
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--------------------------------------------------------------------------------------------------
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This software provides a C++ vector and matrix class with an interface
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to BLAS and ATLAS linear algebra libraries and a few additional features.
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Templates are employed in order to achieve generic applicability of the algorithms.
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In particular, iterative methods suitable for sparse matrices (Davidson diagonalization, linear solvers, matrix exponential)
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can be applied to your custom matrix class, which does not need to contain any explicit
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storage of the matrix (only matrix times vector operation has to be provided).
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(In quantum chemistry, a particular application of this technique is called direct-CI.)
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The library implements reference counting to avoid overhead when copying matrices and passing them by value;
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on the other hand it does NOT optimize matrix expressions, you should yourself call gemv, axpy etc. instead of using operator* etc. where appropriate.
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vec.h, vec.cc : vector class
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mat.h, mat.cc : general matrix class
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smat.h, smat.cc: symmetric matrix class
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vecmat3.h, vecmat3.cc : simplified class for 3-dimensional entities
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quaternion.h, quaternion.cc : quaternions and 3-dim rotations
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sparsemat.h sparsemat.cc: sparse matrix class
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sparsesmat.h sparsesmat.cc: sparse symmetric matrix class
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csrmat.h, csrmat.cc: unfinished work on compressed row format sparse matrices
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nonclass.h nonclass.cc: some methods not belonging to a class - linear solver, diagonalization
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conjgrad.h: conjugate gradient sparse linear solver
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gmres.h: generalized minimal residual sparse linear solver
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matexp.h: matrix exponential (suitable both for dense and sparse matrices - cf. exptimes routine)
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qsort.h: generic quick-sort template
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bitvector.h: bit vector class
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bisection.h: generic bisection search
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diis.h: DIIS convergence acceleration
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davidson.h: Davidson (modified Lanczos) sparse matrix diagonalization
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fourindex.h, fourindex.cc: class for four-index quantities, in particular two-electron integrals in quantum chemistry, allows transparent access to externally stored integrals
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auxstorage.h: class facilitating simple file IO for the vectors and matrices
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permutation.h, qsort.h : permutations and sorting
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INSTALLATION:
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0. autoreconf --install if ./configure does not exist
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1. ./configure with DEBUG, OPTIMIZE and MATPTR options, export CXXFLAGS=-I<path> and export LDFLAGS=-L<path> (and possibly added -latlas -lcblas) 
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   pointing to your preferred BLAS/LAPACK, your preferred --prefix and --enable/--disable options for optimization and debugging
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2. make
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3. make check (to see that the programs t and test compile and link without errors)
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4. make install
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USING:
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1. #include "la.h"
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2. using namespace LA;
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3. look at test.cc and t.cc for examples of use
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4. remember to always call .copyonwrite() before you start writing to individual matrix elements explicitly
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   (when modifying a matrix via provided methods or operators other than () and [], copy on write is performed transparently)
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5. Note that the library does not attempt to do any optimizations of the evaluation of matrix expressions (by template metaprogramming and postponed evaluation techniques), so instead of inefficient "z=A*2*x + y*1.5 " call gemv and axpy methods. On the other hand it makes shallow copies, so you can pass matrix by value to a subroutine and you are allowed to write x=A*x, as a temporary will be created.
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6. you may compile it with DEBUG defined to perform index range and other checks
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NOTE: 
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We named the classes NRMat, NRVec etc. since we started to use them together with the Numerical Recipes library. They can be used with Numerical Recipes in C++, however, they are completely independent of this library.
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This is a beta release. The library has already been in use, but not every function/method has been tested.
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There is no documentation except the source itself, since I originaly did not intend to distribute it.
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I will appreciate your bugfixes, exhancements, suggestions etc.
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Jiri Pittner
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