L Index Page
Keyword Index for the NAG C Library Manual
NAG C Library Manual

Keyword : Least squares

e02agc   Least squares polynomial fit, values and derivatives may be constrained, arbitrary data points
e02bac   Least squares curve cubic spline fit (including interpolation), one variable
e02bec   Least squares cubic spline curve fit, automatic knot placement, one variable
e02cac   Least squares surface fit by polynomials, data on lines parallel to one independent coordinate axis
e02dac   Least squares surface fit, bicubic splines
e02dcc   Least squares bicubic spline fit with automatic knot placement, two variables (rectangular grid)
e02ddc   Least squares bicubic spline fit with automatic knot placement, two variables (scattered data)
e02dhc   Evaluation of spline surface at mesh of points with derivatives
e04ncc   Solves linear least squares and convex quadratic programming problems (non-sparse)
e04unc   Solves nonlinear least squares problems using the sequential QP method
e04yac   Least squares derivative checker for use with e04gbc
e04ycc   Covariance matrix for nonlinear least squares
f08bac   Computes the minimum-norm solution to a real linear least squares problem
f08bnc   Computes the minimum-norm solution to a complex linear least squares problem
f08kac   Computes the minimum-norm solution to a real linear least squares problem using singular value decomposition
f08kcc   Computes the minimum-norm solution to a real linear least squares problem using singular value decomposition (divide-and-conquer)
f08knc   Computes the minimum-norm solution to a complex linear least squares problem using singular value decomposition
f08kqc   Computes the minimum-norm solution to a complex linear least squares problem using singular value decomposition (divide-and-conquer)
f08zac   Solves the real linear equality-constrained least squares (LSE) problem
f08znc   Solves the complex linear equality-constrained least squares (LSE) problem

L Index Page
Keyword Index for the NAG C Library Manual
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford UK. 2012