Publications associées à Sparsity penalized mean-variance portfolio selection: analysis and computation

Publications associées (5)

Area-Oriented Optimization After Standard-Cell Mapping

Giovanni De Micheli, Alessandro Tempia Calvino, Andrea Costamagna

We address the problem of minimizing the area of circuits mapped to a technology library, with or without delay constraints. While traditional methods optimize first a technology-independent representation and then perform technology mapping to a library, ...

Association for Computing Machinery2025

Quantization for Distributed Processing and Learning of Structured Data

Isabela Cunha Maia Nobre

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

EPFL2022

Real-time Control of Battery Energy Storage Systems to Provide Ancillary Services Considering Voltage-Dependent Capability of DC-AC Converters

Mario Paolone, Sidi-Rachid Cherkaoui, Antonio Zecchino, Zhao Yuan

Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. ...

2021

Bayesian optimization of peripheral intraneural stimulation protocols to evoke distal limb movements

Silvestro Micera, Grégoire Courtine, Sophie Marie Marthe Wurth, Simon Borgognon, Marion Aimée Geneviève Badi-Dubois

Objective. Motor neuroprostheses require the identification of stimulation protocols that effectively produce desired movements. Manual search for these protocols can be very time-consuming and often leads to suboptimal solutions, as several stimulation pa ...

IOP Publishing Ltd2021

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Shaul Nadav Hallak

This paper introduces a method for computing points satisfying the second-order necessary optimality conditions for nonconvex minimization problems subject to a closed and convex constraint set. The method comprises two independent steps corresponding to t ...

SPRINGER/PLENUM PUBLISHERS2020