Concept

Matrix norm — where it appears

A single number summarising a matrix's size. It is a summary, so two matrices it calls equally far apart can behave completely differently — which is the reason several quantities on this site are invisible in every norm.

Named by 2 essays across 2 fields — each of them below, with the objects they name alongside it.

the estimator maximises this quantity over the columns it visitscolumn 1 ‹visited›12column 2 ‹the answer›114column 311.4column 411.4column 511.4column 611.4column 711.4column 811.4column 911.4column 1011.4column 1111.4column 1211.4estimate 12.0a walk that stopped earlythe estimate returned12the true 1-norm114columns visited1products with the matrix5the walk's own stopping test firedand every column it could see was smaller

An estimate that can be fooled

Nobody computes a condition number, because forming an inverse costs more than the solve did. Every library estimates it instead, from four or five products with a factorisation already in hand. The estimate is exactly right on four random matrices out of five — and there is a matrix, three distinct entries wide, on which it returns a twentieth of the truth.

error · condition estimation
numbers stored, 256 × 256clustered, strong27,008clustered, weak24,064the dense matrix65,536shuffled, strong65,536shuffled, weak118,208the same matrix, twiceκ, clustered24κ, shuffled24‖A‖_F, clustered6140‖A‖_F, shuffled6140shuffled weak ⁄ dense1.8the compressibility is in the numberingand the numbering is not in the matrix

The same matrix, numbered twice

One symmetric permutation. The condition number is 24.3948 either way to eight digits and the Frobenius norm is 6.13996414·10³ either way to twelve. The partition that stored 27,008 numbers now finds no admissible pair anywhere and stores all 65,536, and the format that compresses regardless stores 118,208.

hierarchy · admissibility

Named alongside it

The objects these essays reach for when they reach for this one.

Condition numberAdmissibilityCluster treeCondition estimationCounterexampleElimination orderFill-reducing orderingHierarchical matrixHilbert matrixLower boundLU factorisationOff diagonal rank

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