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11
Nov 2020

Seminario Doctorado en Ingeniería Industrial e Investigación de Operaciones

Santiago y Viña del Mar
Tipo de Evento: Seminario
Organiza: Facultad de Ingeniería y Ciencias
Dónde: Online, vía Zoom
Público: Académicos
Horario: 11:50:00 hr

El propósito del Seminario DIIIO es permitir que investigadores y profesionales de todo el mundo presenten investigaciones de vanguardia en todas las áreas de Investigación de Operaciones, Ingeniería Industrial y Ciencias de la Gestión a una amplia audiencia, incluidos profesores, estudiantes y cualquier persona interesada.

A las 11:50 expone: Alfredo Torrico (Polytechnique Montreal)

Title:  On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness

Abstract: Submodular maximization has been widely studied over the past decades, mostly because of its numerous applications in real-world problems. It is well known that the standard greedy algorithm guarantees a worst case approximation factor of 1-1/e when maximizing a monotone submodular function under a cardinality constraint. However, empirical studies show that its performance is substantially better in practice. This raises a natural question of explaining this improved performance of the greedy algorithm. In this work, we define sharpness for submodular functions as a candidate explanation for this phenomenon. The sharpness criteria is inspired by the concept of strong convexity in convex optimization. We show that the greedy algorithm provably performs better as the sharpness of the submodular function increases.

Bio: Alfredo Torrico is a postdoctoral researcher at the CERC in Data Science, Polytechnique Montreal. He obtained his Ph.D. in Operations Research in the School of Industrial and Systems Engineering at Georgia Tech in 2019. His main interest is the design of theoretical tools at the interface between discrete and continuous optimization for resource allocation and subset selection problems. Other topics of interest include fairness and diversity, spread of misinformation, and in general, topics on Operations Research/Machine Learning for social good.

Contacto: Diego Morán | diego.moran@uai.cl

Actividad vía Zoom

A las 13:00 hrs. expone: Daniel Olivares (PUC)

Title: A Novel Distributed Control Strategy for Optimal Dispatch of Isolated Microgrids Considering Congestion

Abstract: This work presents a novel distributed control strategy for frequency control, congestion management, and optimal dispatch (OD) in isolated microgrids. The proposed strategy drives the distributed generators (DGs) within the microgrid to a dispatch that complies with the Karush-Kuhn-Tucker (KKT) conditions of a linear optimal power flow (OPF) formulation. The controller relies on local power and frequency measurements, information from neighbouring DGs, and line-flow measurements transmitted through a communications network. Extensive simulations show a good performance of the controller against sudden changes in the load, congested lines and availability of DGs in the microgrid, being able to successfully drive the system to an optimal economic operation.

Bio: Daniel Olivares, es Ingeniero Civil Eléctrico de la Universidad de Chile y Doctor en Ingeniería Eléctrica y Computación de la Universidad de Waterloo, en Ontario, Canadá. En la actualidad es Profesor Asociado del Departamento de Ingeniería Eléctrica de la Pontificia Universidad Católica de Chile, Investigador Asociado del Instituto Sistemas Complejos de Ingeniería (ISCI) e Investigador Asociado del Solar Energy Research Center (SERC-Chile). Su investigación se enfoca en el desarrollo de modelos y técnicas para el control , operación, y planificación de sistemas eléctricos, además del diseño de mercados eléctricos y su regulación. En el ámbito profesional, ha sido consultor para el Banco Mundial, el Ministerio de Energía de Chile, la Comisión Nacional de Energía de Chile, y el Coordinador Eléctrico Nacional, además de otras organizaciones del ámbito privado en el sector eléctrico en Chile.

Contacto: Diego Morán | diego.moran@uai.cl

Actividad vía Zoom

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