Quasi-Newton methods accelerate the steepest-descent technique for function minimization by using computational history to generate a sequence of approximations to the inverse of the Hessian matrix.

The problem of minimizing a concave function subject to linear inequality constraints may have many local solutions. Therefore, finding the global constrained minimum is a computationally difficult ...