BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv6.5.6//EN
X-ORIGINAL-URL:https://cmc.deusto.eus/
X-WR-CALNAME:cmc.deusto.eus
X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-cf5bc9424768595e045fa603f251e534@cmc.deusto.eus
DTSTART:20240628T120000Z
DTEND:20240628T140000Z
DTSTAMP:20251031T212700Z
CREATED:20251031
LAST-MODIFIED:20251031
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:FAU DCN-AvH Seminar
DESCRIPTION:Next Friday June 28, 2024:\nOrganized by: FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\n14:00H\nTitle: RBM-SVRG: A random-sampling based solver for optimal control problems with variance reduction\nSpeaker: Prof. Byungjoon Lee\nAffiliation: The Catholic University of Korea\nAbstract. In this talk, we introduce a new random-sampling based solver for optimal control problems with variance reduction technique, so called RBM-SVRG, inspired by Random Batch Method (RBM) and stochastic variance reduced gradient (SVRG). The proposed algorithm is based on the gradient descent method with adjoint states from Pontryagin’s maximum principle, which requires the computation of the controlled trajectory (forward dynamics) and its adjoint system. To reduce the computational costs on dynamics, we apply a random sampling on the forward dynamics, splitting into simpler randomized ones. Then, from the initial guess of the control, the update of the control function follows the gradient of the randomized cost function as in the stochastic gradient system. Then the variance reduction technique is applied to handle random error from approximation by random sampling. We showed that this optimization process converges to the optimal control of the original system for simple cases, i.e., linear-quadratic optimal control problems. Numerical simulations are also presented to validate the performance of the proposed method.\n14:30H\nTitle: Convergence of Consensus-Based Optimization (CBO) with Random Batch Interactions\nSpeaker: Prof. Dongnam Ko\nAffiliation: The Catholic University of Korea\nAbstract.  In this talk, we analyze the convergence of the consensus-based optimization (CBO) algorithm. This includes the analysis with heterogeneous noise and general interaction network, for example, random batch method. Despite successful performance of CBO in many practical simulations, the analysis confined in kinetic level. This is the first result on the convergence of this algorithm. We start introducing previous formulations and results on CBO models, and then derive stochastic convergence of individual candidate positions to a common point in mean-squre and almost-sure sense under small noise assumption. The analysis uses the concept of ergodicity in stochastic matrix theory. Numerical simulations are also presented to compare convergence speed between different parameters.\nWHEN\nFri. June 28, 2024 at 14:00H\nWHERE\nOn-site: Room 0.151-115 Seminarraum\nElektrotechnik. Friedrich-Alexander-Universität Erlangen-Nürnberg\nCauerstraße 7/9, 91058 Erlangen\nGPS-Koord. Raum: 49.573087N, 11.028468E\n_\nSee all Seminars at FAU DCN-AvH\nDon’t miss out our last news and connect with us!\nLinkedIn | Twitter | Instagram\n
URL:https://cmc.deusto.eus/events-calendar/fau-dcn-avh-seminar/
ORGANIZER;CN=FAU DCN-AvH:MAILTO:
CATEGORIES:FAU DCN-AvH Jr. Seminar,FAU DCN-AvH Seminar
LOCATION:FAU - Faculty of Sciences
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/FAUDCNAvHSeminar_dKoBLee_28jun2024.png
END:VEVENT
END:VCALENDAR