test_nls
test_nls,
a FORTRAN90 code which
defines test problems for
least squares minimization.
The typical problem comprises M functions in N variables,
and it is desired to minimize the sum of squares of the values of
the functions. Each problem is specified by a starting point, a
function and jacobian routines.
The problems include:
-
Linear function, full rank
-
Linear function, rank 1.
-
Linear function, rank 1, zero columns and rows.
-
Rosenbrock function.
-
Helical valley function.
-
Powell singular function.
-
Freudenstein/Roth function.
-
Bard function.
-
Kowalik and Osborne function.
-
Meyer function.
-
Watson function.
-
Box 3-dimensional function.
-
Jennrich and Sampson function.
-
Brown and Dennis function.
-
Chebyquad function.
-
Brown almost-linear function.
-
Osborne function 1.
-
Osborne function 2.
-
Hanson function 1
-
Hanson function 2
-
McKeown function 1
-
McKeown function 2
-
McKeown function 3
-
Devilliers and Glasser function 1
-
Devilliers and Glasser function 2
-
Madsen example
Licensing:
The computer code and data files described and made available on this web page
are distributed under
the MIT license
Languages:
test_nls is available in
a FORTRAN90 version.
Related Data and Programs:
DQED,
a FORTRAN90 code which
seeks the solution of bounded or constrained minimization problems.
MINPACK,
a FORTRAN90 code which
seeks the solution of nonlinear equations, or the least squares minimization
of the residual.
test_nls_test
Reference:
-
John Dennis, David Gay, and Phuong Vu,
A new nonlinear equations test problem,
Technical Report 83-16,
Mathematical Sciences Department,
Rice University, (1983 - revised 1985).
-
N. de Villiers and D. Glasser,
A continuation method for nonlinear regression,
SIAM Journal of Numerical Analysis,
Volume 18, pages 1139-1154, 1981.
-
C. Fraley,
Solution of nonlinear least-squares problems,
Technical Report STAN-CS-1165,
Computer Science Department,
Stanford University, 1987.
-
C. Fraley,
Software performance on nonlinear least-squares problems,
Technical Report SOL 88-17,
Systems Optimization Laboratory,
Department of Operations Research,
Stanford University, 1988.
-
JJ McKeown,
Specialized versus general-purpose algorithms for functions that
are sums of squared terms,
Math. Prog.,
Volume 9, pages 57-68, 1975.
-
JJ McKeown,
On algorithms for sums of squares problems,
in Towards Global Optimization,
L. C. W. Dixon and G. Szego (editors),
North-Holland, pages 229-257, 1975.
-
Jorge More, Burton Garbow, Kenneth Hillstrom,
Testing unconstrained optimization software,
ACM Transactions on Mathematical Software,
Volume 7, Number 1, March 1981, pages 17-41.
-
Jorge More, Burton Garbow, Kenneth Hillstrom,
Algorithm 566:
FORTRAN Subroutines for Testing unconstrained optimization software,
ACM Transactions on Mathematical Software,
Volume 7, Number 1, March 1981, pages 136-140.
-
Douglas Salane,
A continuation approach for solving large residual nonlinear
least squares problems,
SIAM Journal of Scientific and Statistical Computing,
Volume 8, pages 655-671, 1987.
Source Code:
Last revised on 04 September 2020.