cgrdh - Evaluate gradient of objective or Lagrangian function, gradients of general constraint functions, and Hessian of Lagrangian
CGRDH Evaluate gradient of objective or Lagrangian function, gradients of general constraint functions, and Hessian of Lagrangian.
[g,cjac,h]=cgrdh(x,v) returns the gradient of the objective function in g, the gradients of the general constraint functions in cjac, and the Hessian of the Lagrangian in h. The Hessian is stored as a full matrix. cjac(i,j) contains the partial derivative of the i-th constraint with respect to the j-th variable.
[g,cjac,h]=cgrdh(x,v,options), where options is a 2-dimensional vector, allows cjac to be transposed and requests the gradient of the Lagrangian to be placed in g.
options( 1 ) = jtrans, set to 1 if the user wants the transpose of the Jacobian, where the i,j-th component is the partial derivative of the j-th constraint with respect to the i-th variable. If options is not given, jtrans defaults to 0.
options( 2 ) = grlagf, set to 1 if the gradient of the Lagrangian is required and set to 0 if the gradient of the objective function is sought. If options is not given, grlagf defaults to 0.
sifdecode(get_sif_path()+'sif/BT1.SIF',TMPDIR+'/BT1') buildprob(TMPDIR+'/BT1') [x,bl,bu,v,cl,cu,equatn,linear] = csetup(TMPDIR+'/BT1/OUTSDIF.d'); [G,CJAC,H]=cgrdh(x,v)
Nicholas I.M. Gould - n.gould@rl.ac.uk - RAL
Dominique Orban - orban@ece.northwestern.edu - Northwestern
Philippe L. Toint - Philippe.Toint@fundp.ac.be - FUNDP
see http://hsl.rl.ac.uk/cuter-www