Bandwidth of a matrix: Difference between revisions
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Suppose <math>n</math> is a positive integer and <math>A = (a_{ij})_{1 \le i \le n, 1 \le j \le n}</math> is a <math>n \times n</math> [[square matrix]].  | Suppose <math>n</math> is a positive integer and <math>A = (a_{ij})_{1 \le i \le n, 1 \le j \le n}</math> is a <math>n \times n</math> [[square matrix]].  | ||
The '''left half-bandwidth''' of <math>A<math> is defined as the smallest positive integer <math>k_1</math> such that <math>a_{ij} = 0</math> whenever <math>j < i - k_1</math>. In other words, entries that are more than <math>k_1</math> positions below the [[main diagonal]] are zero.  | The '''left half-bandwidth''' of <math>A</math> is defined as the smallest positive integer <math>k_1</math> such that <math>a_{ij} = 0</math> whenever <math>j < i - k_1</math>. In other words, entries that are more than <math>k_1</math> positions below the [[main diagonal]] are zero.  | ||
The '''right half-bandwidth''' of <math>A</math> is defined as the smallest positive integer <math>k_2</math> such that <math>a_{ij} = 0</math> whenever <math>j < i + k_2</math>. In other words, entries that are more than <math>k_2</math> positions above the [[main diagonal]] are zero.  | The '''right half-bandwidth''' of <math>A</math> is defined as the smallest positive integer <math>k_2</math> such that <math>a_{ij} = 0</math> whenever <math>j < i + k_2</math>. In other words, entries that are more than <math>k_2</math> positions above the [[main diagonal]] are zero.  | ||
Revision as of 17:16, 1 May 2014
Definition
Suppose is a positive integer and is a square matrix.
The left half-bandwidth of is defined as the smallest positive integer such that whenever . In other words, entries that are more than positions below the main diagonal are zero.
The right half-bandwidth of is defined as the smallest positive integer such that whenever . In other words, entries that are more than positions above the main diagonal are zero.
The banwidth is defined as , where are the left and right half-bandwidths respectively.
Ambiguity with terminology
When we say that a matrix has a given bandwidth (respectively, a given left half-bandwidth or a given right half-bandwidth) what we mean is that the bandwidth (respectively, the left or right half-bandwidth) is at most that quantity, not that it is necessarily exactly equal to that quantity.
Particular cases
| Matrix type (all matrices are ) | Left half-bandwidth (upper bound) | Right half-bandwidth (upper bound) | Bandwidth (upper bound) | 
|---|---|---|---|
| diagonal matrix | 0 | 0 | 1 | 
| tridiagonal matrix | 1 | 1 | 3 | 
| pentadiagonal matrix | 2 | 2 | 5 | 
| upper triangular matrix | 0 | ||
| lower triangular matrix | 0 |