TheAlgorithms/C++
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All the algorithms implemented in C++
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lu_decomposition.h
Go to the documentation of this file.
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#pragma once
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#include <iostream>
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#include <valarray>
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#include <vector>
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#ifdef _OPENMP
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#include <omp.h>
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#endif
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template
<
typename
T>
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using
matrix
= std::vector<std::valarray<T>>;
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template
<
typename
T>
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int
lu_decomposition
(
const
matrix<T>
&A,
matrix<double>
*L,
matrix<double>
*U) {
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int
row, col, j;
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int
mat_size
= A.size();
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if
(
mat_size
!= A[0].size()) {
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// check matrix is a square matrix
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std::cerr <<
"Not a square matrix!\n"
;
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return
-1;
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}
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// regularize each row
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for
(row = 0; row <
mat_size
; row++) {
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// Upper triangular matrix
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for
(col = row; col <
mat_size
; col++) {
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// Summation of L[i,j] * U[j,k]
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double
lu_sum = 0.;
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for
(j = 0; j < row; j++) {
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lu_sum += L[0][row][j] * U[0][j][col];
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}
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// Evaluate U[i,k]
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U[0][row][col] = A[row][col] - lu_sum;
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}
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// Lower triangular matrix
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for
(col = row; col <
mat_size
; col++) {
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if
(row == col) {
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L[0][row][col] = 1.;
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continue
;
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}
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// Summation of L[i,j] * U[j,k]
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double
lu_sum = 0.;
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for
(j = 0; j < row; j++) {
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lu_sum += L[0][col][j] * U[0][j][row];
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}
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// Evaluate U[i,k]
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L[0][col][row] = (A[col][row] - lu_sum) / U[0][row][row];
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}
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}
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return
0;
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}
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template
<
typename
T>
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double
determinant_lu
(
const
matrix<T>
&A) {
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matrix<double>
L(A.size(), std::valarray<double>(A.size()));
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matrix<double>
U(A.size(), std::valarray<double>(A.size()));
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if
(
lu_decomposition
(A, &L, &U) < 0)
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return
0;
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double
result = 1.f;
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for
(
size_t
i = 0; i < A.size(); i++) {
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result *= L[i][i] * U[i][i];
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}
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return
result;
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}
determinant_lu
double determinant_lu(const matrix< T > &A)
Definition
lu_decomposition.h:90
lu_decomposition
int lu_decomposition(const matrix< T > &A, matrix< double > *L, matrix< double > *U)
Definition
lu_decomposition.h:29
matrix
std::vector< std::valarray< T > > matrix
Definition
lu_decomposition.h:19
mat_size
ll mat_size
Definition
matrix_exponentiation.cpp:45
numerical_methods
lu_decomposition.h
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