Penalty methods for the solution of generalized Nash equilibrium problems and hemivariational inequalities with VI constraints

In this thesis we propose penalty methods for the solution of Generalized Nash Equilibrium Problems (GNEPs) and we consider centralized and distributed algorithms for the solution of Hemivariational Inequalities (HVIs) where the feasible set is given by the intersection of a closed convex set with the solution set of a lower-level monotone Variational Inequality (VI)

Penalty methods for the solution of generalized Nash equilibrium problems and hemivariational inequalities with VI constraints

In this thesis we propose penalty methods for the solution of Generalized Nash Equilibrium Problems (GNEPs) and we consider centralized and distributed algorithms for the solution of Hemivariational Inequalities (HVIs) where the feasible set is given by the intersection of a closed convex set with the solution set of a lower-level monotone Variational Inequality (VI)

Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity

We consider nonconvex constrained optimization problems and propose a new approach to the convergence analysis based on penalty functions. We make use of classical penalty functions in an unconventional way, in that penalty functions only enter in the theoretical analysis of convergence while the algorithm itself is penalty-free. Based on this idea, we are able to establish several new results, including the first general analysis for diminishing stepsize methods in nonconvex, constrained optimization, showing convergence to generalized stationary points, and a complexity study for SQP-type algorithms.Comment: To appear on Mathematics of Operations Researc

Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case

In this paper we first extend the diminishing stepsize method for nonconvex constrained problems presented in [4] to deal with equality constraints and a nonsmooth objective function of composite type. We then consider the particular case in which the constraints are convex and satisfy a standard constraint qualification and show that in this setting the algorithm can be considerably simplified, reducing the computational burden of each iteration.Comment: arXiv admin note: text overlap with arXiv:1709.0338

Electromechanical Actuators Affected by Multiple Failures: Prognostic Method based on Wavelet Analysis Techniques

Incipient failures of electromechanical actuators (EMA) of primary flight command, especially if related to progressive evolutions, can be identified with the employment of several different approaches. A strong interest is expected by the development of prognostic algorithms capable of identifying precursors of EMA progressive failures: indeed, if the degradation pattern is properly identified, it is possible to trig an early alert, leading to proper maintenance and servomechanism replacement. Given that these algorithms are strictly technology-oriented, they may show great effectiveness for some specific applications, while they could fail for other applications and technologies: therefore, it is necessary to conceive the prognostic method as a function of the considered failures. This work proposes a new prognostic strategy, based on artificial neural networks, able to perform the fault detection and identification of two EMA motor faults (i.e. coil short circuit and rotor static eccentricity). In order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by means of Discrete Wavelet Transform extracting several features: in fact, these wavelets result very effective to detect fault condition, both in time domain and frequency domain, by means of the change in amplitude and shape of its coefficients. A simulation test bench has been developed for the purpose, demonstrating that the method is robust and is able to early identify incoming failures, reducing the possibility of false alarms or non-predicted problems

Partial penalization for the solution of generalized Nash equilibrium problems

Nash equilibrium problem, Generalized Nash equilibrium problem, Jointly convex problem, Exact penalty function, Partial penalization,