Chapter 8 Optimization
Routines
Unconstrained Minimization
Univariate Function
Using function values only
, UVMIFUsing function and first derivative values
, UVMIDNonsmooth function
, UVMGSMultivariate 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
, UMCGGNonsmooth function
, UMPOLNonlinear Least Squares
Using finite-difference Jacobian
, UNLSFUsing analytic Jacobian
, UNLSJMinimization with Simple Bounds
Using finite-difference gradient
, BCONFUsing analytic gradient
, BCONGUsing finite-difference Hessian
, BCODHUsing analytic Hessian
, BCOAHNonsmooth Function
, BCPOLNonlinear least squares using finite-difference Jacobian
, BCLSFNonlinear least squares using analytic Jacobian
, BCLSJNonlinear least squares problem subject to bounds.
, BCNLSLinearly 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
, LCONGNonlinearly Constrained Minimization
Using a sequential equality constrained QP method
, NNLPFUsing a sequential equality constrained QP method
with user-supplied gradients
, NNLPGService 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
, GGUES