Two limited-memory optimization methods with minimum violation of the previous quasi-Newton equations

0532367 - ÚI 2021 CZ eng V - Výzkumná zpráva
Vlček, Jan - Lukšan, Ladislav
Two limited-memory optimization methods with minimum violation of the previous quasi-Newton equations.
Prague: ICS CAS, 2020. 21 s. Technical Report, V-1280.
Institucionální podpora: RVO:67985807
Klíčová slova: unconstrained minimization * variable metric methods * limited-memory methods * variationally derived methods * global convergence * numerical results

Limited-memory variable metric methods based on the well-known BFGS update are widely used for large scale optimization. The block version of the BFGS update, derived by Schnabel (1983), Hu and Storey (1991) and Vlček and Lukšan (2019), satisfies the quasi-Newton equations with all used difference vectors and for quadratic objective functions gives the best improvement of convergence in some sense, but the corresponding direction vectors are not descent directions generally. To guarantee the descent property of direction vectors and simultaneously violate the quasi-Newton equations as little as possible in some sense, two methods based on the block BFGS update are proposed. They can be advantageously combined with methods based on vector corrections for conjugacy (Vlček and Lukšan, 2015). Global convergence of the proposed algorithm is established for convex and sufficiently smooth functions. Numerical experiments demonstrate the efficiency of the new methods.
Trvalý link: http://hdl.handle.net/11104/0310865