Numerical Linear Algebra
where the algebra stops being the arithmetic
Concept

Least squares — where it appears

Minimising ‖Ax − b‖ over x, which assumes the matrix is exact and applies the whole of the correction to the right-hand side.

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

everything Ax can reachb = (1.1, 0.4, 1.5)Ax, the closest reachable pointr = b − Ax‖Aᵀr‖ / (‖A‖‖r‖)1.7·10⁻¹⁶‖b‖² − ‖Ax‖² − ‖r‖²1.3·10⁻¹⁵‖r‖1.3200 random nearby points of the plane were tried; none is closer.a 3×2 system, Householder QRperpendicularity is checked

The projection and the right angle

The least-squares solution is the one whose residual is perpendicular to everything the columns can reach. That is not a mnemonic — it is an equation, Aᵀr = 0, and the computed answer satisfies it to 10⁻¹⁶.

leastsquares · least squares 00.250.50.75110⁻¹110¹share of the noise placed in the matrixleast-squares error ÷ total least-squares errorequally accuratetotal leastsquares aheadordinary leastsquares aheadthe model, not the methodadvantage, all noise in b0.28advantage, all noise in A2.5seeds at each share40the same total noise at every pointand only where it sits changes

When the matrix is wrong too

Every least-squares problem on this site has assumed A is exact and b is not, and moved b onto the column space of A. Where both were measured, the smallest correction that makes the system consistent moves the matrix as well — and on the problems where that answer is more accurate, it has the larger residual, by construction rather than by luck.

leastsquares · total least squares

Named alongside it

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

Normal equationsOrthogonal projectionResidualCondition numberEckart–YoungErrors-in-variablesForward errorGram matrixOrthogonalitySingular value decompositionTotal least squares

All concepts