cgr - Evaluate gradient of the objective or Lagrangian function, and the gradients of the general constraint functions
cgr Evaluate gradient of the objective or Lagrangian function, and the gradients of the general constraint functions.
[g,cjac]=cgr(x) returns the gradient of the objective function in g and the gradients of the general constraint functions in cjac. cjac(i,j) contains the partial derivative of the i-th constraint with respect to the j-th variable.
[g,cjac]=cgr(x,v) returns the gradient of the Lagrangian function in g and the gradients of the general constraint functions in cjac, where x is the current estimate of the solution and v is the current estimate of the Lagrange multipliers.
[g,cjac]=cgr(x,v,options), where options is a 2-dimensional boolean vector, allows cjac to be transposed and requests the gradient of the Lagrangian to be placed in g.
options( 1 ) = jtrans, set to %t 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 %f.
options( 2 ) = grlagf, set to %t if the gradient of the Lagrangian is required and set to %f if the gradient of the objective function is sought. Note that grlagf defaults to %f if v is not given, and defaults to 1 if v is given.
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] = cgr(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