Parallel block coordinate minimization with application to group regularized regression

This paper proposes a method for parallel block coordinate-wise minimization of convex functions. Each iteration involves a first phase where n independent minimizations are performed over the n variable blocks, followed by a phase where the results of the first phase are coordinated to obtain the whole variable update. Convergence of the method to the global optimum is proved for functions composed of a smooth part plus a possibly non-smooth but separable term. The method is also proved to have a linear rate of convergence, for functions that are smooth and strongly convex. The proposed algorithm can give computational advantage over the more standard serial block coordinate-wise minimization methods, when run over a parallel, multi-worker, computing architecture. The method is suitable for regularized regression problems, such as the group Lasso, group Ridge regression, and group Elastic Net. Numerical tests are run on such types of regression problems to exemplify the performance of the proposed method

Optimal Dynamic Asset Allocation with Lower Partial Moments Criteria and Affine Policies

This paper discusses an optimization-based approach for solving multi-period dynamic asset allocation problems using empirical asymmetric measures of risk. Three features distinguish the proposed approach from the mainstream ones. First, our approach is non parametric, in the sense that it does not require explicit estimation of the parameters of a statistical model for the returns distribution: the approach relies directly on data (the scenarios) generated by an oracle which may include expert knowledge along with a standard stochastic return model. Second, it employs affine decision policies, which make the multi-period formulation of the problem amenable to an efficient convex optimization format. Third, it uses asymmetric, unilateral measures of risk which, unlike standard symmetric measures such as variance, capture the fact that investors are usually not averse to return deviations from the expected target, if these deviations actually exceed the target

A guaranteed-convergence framework for passivity enforcement of linear macromodels

Passivity enforcement is a key step in the extraction of linear macromodels of electrical interconnects and packages for Signal and Power Integrity applications. Most state-of-the-art techniques for passivity enforcement are based on suboptimal or approximate formulations that do not guarantee convergence. We introduce in this paper a new rigorous framework that casts passivity enforcement as a convex non-smooth optimization problem. Thanks to convexity, we are able to prove convergence to the optimal solution within a finite number of steps. The effectiveness of this approach is demonstrated through various numerical example

Subgradient Techniques for Passivity Enforcement of Linear Device and Interconnect Macromodels

This paper presents a class of nonsmooth convex optimization methods for the passivity enforcement of reduced-order macromodels of electrical interconnects, packages, and linear passive devices. Model passivity can be lost during model extraction or identification from numerical field solutions or direct measurements. Nonpassive models may cause instabilities in transient system-level simulation, therefore a suitable postprocessing is necessary in order to eliminate any passivity violations. Different from leading numerical schemes on the subject, passivity enforcement is formulated here as a direct frequency-domain ${{cal H}_infty}$ norm minimization through perturbation of the model state-space parameters. Since the dependence of this norm on the parameters is nonsmooth, but continuous and convex, we resort to the use of subdifferentials and subgradients, which are used to devise two different algorithms. We provide a theoretical proof of the global optimality for the solution computed via both schemes. Numerical results confirm that these algorithms achieve the global optimum in a finite number of iterations within a prescribed accuracy leve

Model Predictive Control of stochastic LPV Systems via Random Convex Programs

This paper considers the problem of stabilization of stochastic Linear Parameter Varying (LPV) discrete time systems in the presence of convex state and input constraints. By using a randomization approach, a convex finite horizon optimal control problem is derived, even when the dependence of the system's matrices on the time-varying parameters is nonlinear. This convex problem can be solved efficiently, and its solution is a-priori guaranteed to be probabilistically robust, up to a user-defined probability level p. Then, a novel receding horizon control strategy that involves, at each time step, the solution of a finite-horizon scenario-based control problem, is proposed. It is shown that the resulting closed loop scheme drives the state to a terminal set in finite time, either deterministically, or with probability no less than p. The features of the approach are shown through a numerical exampl

Robust Model Predictive Control via Random Convex Programming

This paper proposes a new approach to design a robust model predictive control (MPC) algorithm for LTI discrete time systems. By using a randomization technique, the optimal control problem embedded in the MPC scheme is solved for a finite number of realizations of model uncertainty and additive disturbances. Theoretical results in random convex programming (RCP) are used to show that the designed controller achieves asymptotic closed loop stability and constraint satisfaction, with a guaranteed level of probability. The latter can be tuned by the designer to achieve a tradeoff between robustness and computational complexity. The resulting Randomized MPC (RMPC) technique requires quite mild assumptions on the characterization of the uncertainty and disturbances and it involves a convex optimization problem to be solved at each time step. The technique is applied here to a case study of an electro-mechanical positioning syste

Age structure in SIRD models for the COVID-19 pandemic—A case study on Italy data and effects on mortality

The COVID-19 pandemic is bringing disruptive effects on the healthcare systems, economy and social life of countries all over the world. Even though the elder portion of the population is the most severely affected by the COVID-19 disease, the counter-measures introduced so far by governments took into little account the age structure, with restrictions that act uniformly on the population irrespectively of age. In this paper, we introduce a SIRD model with age classes for studying the impact on the epidemic evolution of lockdown policies applied heterogeneously on the different age groups of the population. The proposed model is then applied to age-stratified COVID-19 Italian data. The simulation results suggest that control measures focused to specific age groups may bring benefits in terms of reduction of the overall mortality rate

Efficient model-free Q-factor approximation in value space via log-sum-exp neural networks

We propose an efficient technique for performing data-driven optimal control of discrete-time systems. In particular, we show that log-sum-exp ($lse$) neural networks, which are smooth and convex universal approximators of convex functions, can be efficiently used to approximate Q-factors arising from finite-horizon optimal control problems with continuous state space. The key advantage of these networks over classical approximation techniques is that they are convex and hence readily amenable to efficient optimization

A Variation on a Random Coordinate Minimization Method for Constrained Polynomial Optimization

In this paper, an algorithm is proposed for solving constrained and unconstrained polynomial minimization problems. The algorithm is a variation on random coordinate descent, in which transverse steps are seldom taken. Differently from other methods available in the literature, the proposed technique is guaranteed to converge in probability to the global solution of the minimization problem, even when the objective polynomial is nonconvex. The theoretical results are corroborated by a complexity analysis and by numerical tests that validate its efficiency

Delay Robustness of Consensus Algorithms: Continuous-Time Theory

Consensus among autonomous agents is a key problem in multiagent control. In this article, we consider averaging consensus policies over time-varying graphs in presence of unknown but bounded communication delays. It is known that consensus is established (no matter how large the delays are) if the graph is periodically, or uniformly quasi-strongly connected (UQSC). The UQSC condition is often believed to be the weakest sufficient condition under which consensus can be proved. We show that the UQSC condition can actually be substantially relaxed and replaced by a condition that we call aperiodic quasi-strong connectivity, which, in some sense, proves to be very close to the necessary condition (the so-called integral connectivity). Under the assumption of reciprocity of interactions (e.g., for undirected or type-symmetric graphs), a necessary and sufficient condition for consensus in presence of bounded communication delays is established; the relevant results have been previously proved only in the undelayed case