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LU Decomposition. Greg Beckham, Michael Sedivy. Overview. Step 1. This is handled implicitly in the code by only calculating the diagonal for β. Step 2. Calculating β ij. for(j = 0; j < n; j++) // This is the loop over columns of Crout\'s method {

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lu decomposition

LU Decomposition

Greg Beckham, Michael Sedivy

step 1
Step 1

This is handled implicitly in the code by only calculating the diagonal for β

calculating ij
Calculating βij

for(j = 0; j < n; j++) // This is the loop over columns of Crout\'s method

{

for(i = 0; i < j; i++) // Equation (2.3.12) except for i = j

{

sum = a[i][j];

for(k = 0; k < i; k++) sum -= a[i][k] * a[k][j];

a[i][j] = sum;

}

}

slide7

for(j = 0; j < n; j++) // This is the loop over columns of Crout\'s method

{

for(i = j; i < n; i++)// This is i=j of equation (2.3.12) and i=j+1

{ // N-1 of equation (2.3.13)

sum = a[i][j];

for(k = 0; k < j; k++) sum -= a[i][k] * a[k][j];

a[i][j] = sum;

}

if(j != n - 1) // Divide by the pivot element

{

dum = 1.0/(a[j][j]);

for(i = j + 1; i < n; i++) a[i][j] *= dum;

}

}

pivoting
Pivoting
  • Initially finds largest element in each row
  • Used as a “scaling factor”, not sure of use other than to rollover

for(i = 0; i < n; i++) // Loop over the rows to get implicit scaling

{ // information

big = 0.0;

for(j = 0; j < n; j++)

{

if((temp = fabs(a[i][j])) > big) big = temp;

}

if (big == 0.0)

{

printf("ERROR: Singular matrix\n");

}

// non-zero largest element.

vv[i] = 1.0/big; // Save the scaling

}

pivoting1
Pivoting
  • Finds maximum

if((dum = vv[i] * fabs(sum)) >= big)

{

// Is the figure of merit for the pivot better than the best so far?

big = dum;

imax = i;

}

pivoting2
Pivoting
  • Performs row interchanges

if(j != imax) // Do we need to interchange rows?

{

for(k = 0; k < n; k++) // Interchange rows

{

dum = a[imax][k];

a[imax][k] = a[j][k];

a[j][k] = dum;

}

d = -d; // change the parity of d

vv[imax] = vv[j]; // interchange scale factor

}

indx[j] = imax;

related questions
Related Questions
  • What is the advantage of LU(P) solver over GJ(P) solver? (Complexity)
    • Both are O(N3)
    • After LU(P) is solved, more solutions supposed to be found in O (N2)
  • Are you keeping L and U in the same matrix, or separate? Advantage/disadvantage?
    • LU are being created in place in the same matrix.
    • The advantage to this strategy is lower memory usage
    • The disadvantage is that the original matrix is lost
  • I am somewhat confused with extraction of P in decomposition, and how it is then used in eq solving. Can you elaborate more?
related questions1
Related Questions
  • Cormen et al., p 824, used a single array instead of P. Needs careful explanation.
    • From Cormen et al. 825. “we dynamically maintain the permutation matrix P as an array π, where π[i] = j means that the ith row of P contains a 1 in column j

|2| | 0 1 0 |

π = |3| => P | 0 0 1 |

|1| | 1 0 0 |

related questions2
Related Questions
  • How complex equations are solved? (in Text)
    • If only the right hand side vector is complex then the operation can be performed by solving for the real part, then the imaginary
    • If the matrix itself is complex then
      • Rewrite the algorithm for complex values
      • Split the real and imaginary parts into separate real number and solve using existing algorithm
        • A * x – C * y = b
        • C * x + A * y = b
gj vs lup
GJ vs. LUP

Average Difference is 2.765471

Average Difference is 1.255924

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