Band Matrix
An important special type of sparse matrices is band matrix, defined as follows. The lower bandwidth of a matrix A is the smallest number p such that the entry aij vanishes whenever i > j + p. Similarly, the upper bandwidth is the smallest p such that aij = 0 whenever i < j − p (Golub & Van Loan 1996, §1.2.1). For example, a tridiagonal matrix has lower bandwidth 1 and upper bandwidth 1. As another example, the following sparse matrix has lower and upper bandwidth both equal to 3. Notice that zeros are represented with dots.
Matrices with reasonably small upper and lower bandwidth are known as band matrices and often lend themselves to simpler algorithms than general sparse matrices; or one can sometimes apply dense matrix algorithms and gain efficiency simply by looping over a reduced number of indices.
By rearranging the rows and columns of a matrix A it may be possible to obtain a matrix A’ with a lower bandwidth. A number of algorithms are designed for bandwidth minimization.
Read more about this topic: Sparse Vector
Famous quotes containing the words band and/or matrix:
“Citizen’s Band radio renders one accessible to a wide variety of people from all walks of life. It should not be forgotten that all walks of life include conceptual artists, dry cleaners, and living poets.”
—Fran Lebowitz (b. 1950)
““The matrix is God?”
“In a manner of speaking, although it would be more accurate ... to say that the matrix has a God, since this being’s omniscience and omnipotence are assumed to be limited to the matrix.”
“If it has limits, it isn’t omnipotent.”
“Exactly.... Cyberspace exists, insofar as it can be said to exist, by virtue of human agency.””
—William Gibson (b. 1948)