A NONMONOTONE ADMM-BASED DIAGONAL QUASI-NEWTON UPDATE WITH APPLICATION TO THE COMPRESSIVE SENSING PROBLEM

Abstract

Considering a minimization problem according to the Byrd-Nocedal measure function together with the secant equation, a diagonal quasi-Newton updating formula is suggested. To find the optimal elements of the updating matrix, the well-known algorithm of the alternating direction method of multipliers (ADMM) is employed. Moreover, convergence analysis is conducted based on a modified nonmonotone Armijo line search incorporating the simulated annealing strategy. Lastly, performance of the method is numerically tested on a set of CUTEr functions and on a smooth transcendental approximation of the compressive sensing problem. Across the computational spectrum, the given method turns out to be successful.