Quadratic Approximations in Convex Nondifferentiable Optimization
Manlio Gaudioso, Maria Flavia Monaco · SIAM Journal on Control and Optimization · 1991
An implementable descent method for the unconstrained minimization of convex nonsmooth functions of several variables is described. The algorithm is characterized by the use of a set of quadratic approximations of the objective function in order to compute the search direction. The resulting direction finding subproblem is shown to be equivalent to a structured parametric quadratic programming problem. The convergence of the algorithm to the minimum is proved, and numerical experience is reported.