Optimization
Routines
8.1. Unconstrained Minimization
8.1.1 Univariate Function
Using function values only
UVMIFUsing function and first derivative values
UVMID8.1.2 Multivariate Function
Using finite-difference gradient
UMINFUsing analytic gradient
UMINGUsing finite-difference Hessian
UMIDHUsing analytic Hessian
UMIAHUsing conjugate gradient with finite-difference gradient
UMCGFUsing conjugate gradient with analytic gradient
UMCGG8.1.3 Nonlinear Least Squares
Using finite-difference Jacobian
UNLSFUsing analytic Jacobian
UNLSJ8.2. Minimization with Simple Bounds
Using finite-difference gradient
BCONFUsing analytic gradient
BCONGUsing finite-difference Hessian
BCODHUsing analytic Hessian
BCOAHNonlinear least squares using finite-difference Jacobian
BCLSFNonlinear least squares using analytic Jacobian
BCLSJNonlinear least squares problem subject to bounds.
BCNLS8.3. Linearly Constrained Minimization
Reads an MPS file containing a linear programming problem
or a quadratic programming problem
READ_MPSDeallocates the space allocated for the IMSL derived type
s_MPS.
MPS_FREEDense linear programming
DLPRSSparse linear programming
SLPRSSolves a transportation problem
TRANQuadratic programming
QPROGGeneral objective function with finite-difference gradient
LCONFGeneral objective function with analytic gradient
LCONG8.4. Nonlinearly Constrained Minimization
Using a sequential equality constrained QP method
NNLPFUsing a sequential equality constrained QP method
with user-supplied gradients
NNLPG8.5. Service Routines
Central-difference gradient
CDGRDForward-difference gradient
FDGRDForward-difference Hessian
FDHESForward-difference Hessian using analytic gradient
GDHESDivided-finite difference Jacobian
DDJACForward-difference Jacobian
FDJACCheck user-supplied gradient
CHGRDCheck user-supplied Hessian
CHHESCheck user-supplied Jacobian
CHJACGenerate starting points
GGUESPublished date: 03/19/2020
Last modified date: 03/19/2020