Here 11 is an optimization precision , oc is a step size chosen with the strong Wolfe 's rule ( Nocedal and Wright 1999 ) .
numerical optimization
Jorge Nocedal Stephen J Wright
We optimize the dual objective using the gradient based methods shown in Algorithm 1 . Here 11 is an optimization precision , oc is a step size chosen with the strong Wolfe 's rule ( Nocedal and Wright 1999 ) . Here , PV ( A ) represents an ascent direction chosen as follows : For inequality constraints , it is the projected gradient ( Bertsekas 1999 ) ; for equality constraints with slack , we use conjugate gradient ( Nocedal and Wright 1999 ) , noting that when A = 0 , the objective is not differentiable .
Here 11 is an optimization precision , oc is a step size chosen with the strong Wolfe 's rule ( @@CITATION ) .