Solve.banded: Solution of a banded system of linear equations

In limSolve: Solving Linear Inverse Models

Solution of a banded system of linear equations

Description

Solves the linear system of equations

Ax = B

by Gaussion elimination

where A has to be square, and banded, i.e. with the only nonzero elements in bands near the diagonal.

The matrix A is either inputted as a full square matrix or as the non-zero bands.

uses lapack subroutine dgbsv (FORTRAN)

Usage

Solve.banded(abd, nup, nlow, B = rep(0, times = ncol(abd)),
  full = (nrow(abd) == ncol(abd)))

Arguments

abd	either a matrix containing the (nonzero) bands, rotated row-wise (anti-clockwise) only, or a full square matrix.
nup	number of nonzero bands above the diagonal; ignored if full matrix is inputted.
nlow	number of nonzero bands below the diagonal; ignored if full matrix is inputted.
B	Right-hand side of the equations, a vector with length = number of rows of A, or a matrix with number of rows = number of rows of A.
full	if TRUE: full matrix is inputted, if FALSE: banded matrix is input.

Details

If the input matrix abd is square, it is assumed that the full, square A is inputted, unless full is set to FALSE.

If abd is not square, then the number of columns denote the number of unknowns, while the number of rows equals the nonzero bands, i.e. nup+nlow+1

Value

matrix with the solution, X, of the banded system of equations A X =B, the number of columns of this matrix = number of columns of B.

Note

A similar function but that requires a totally different input can now also be found in the Matrix package

Author(s)

Karline Soetaert <[e-mail]>

References

J.J. Dongarra, J.R. Bunch, C.B. Moler, G.W. Stewart, LINPACK Users' Guide, SIAM, 1979.

See Also

Solve.tridiag to solve a tridiagonal system of linear equations.

Solve the generalised inverse solution,

solve the R default

Examples

# 1. Generate a banded matrix of random numbers, full format
nup  <- 2                         # nr nonzero bands above diagonal
ndwn <- 3                         # nr nonzero bands below diagonal
nn   <- 10                        # nr rows and columns of A
A <- matrix(nrow = nn, ncol = nn, data = runif(1 : (nn*nn)))
A [row(A) < col(A) - nup | row(A) > col(A) + ndwn] <- 0 
diag(A) <- 1                      # 1 on diagonal is easily recognised 

# right hand side
B <- runif(nrow(A))                

# solve it, using the default solver and banded (inputting full matrix)
Full  <- solve(A, B)
Band1 <- Solve.banded(A, nup, ndwn, B)

# 2. create banded form of matrix A
Aext <- rbind(matrix(ncol = ncol(A), nrow = nup, 0),
              A, 
              matrix(ncol = ncol(A), nrow = ndwn, 0))
              
abd  <- matrix(nrow = nup + ndwn + 1, ncol = nn,
               data = Aext[col(Aext) <= row(Aext) & 
                           col(Aext) >= row(Aext) - ndwn - nup])

# print both to screen
A
abd

# solve problem with banded version
Band2 <- Solve.banded(abd, nup, ndwn, B)

# compare 3 methods of solution
cbind(Full, Band1, Band2)

# same, now with 3 different right hand sides
B3 <- cbind(B, B*2, B*3)
Solve.banded(abd, nup, ndwn, B3)

limSolve documentation built on Sept. 22, 2023, 1:07 a.m.