Evaluate gradient of the objective or Lagrangian function, and the gradients of the general constraint functions
[g,cjac] = cgr(x,v,options)
real vector, problem variable
v is the current estimate of the Lagrange multipliers
a 2-dimensional boolean vector
gradient of the objective function
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.
Based on CUTEr authored by
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