[vc_row el_id=”top”][vc_column][proconf_onepage_nav nav_logo=”http://workshop.mathos.unios.hr/wp-content/uploads/2017/12/plavo_geo_OM2-1.png” nav_menu=”one-page-nav-menu”][/vc_column][vc_column][/vc_column][/vc_row][vc_section full_width=”container-wide” padding_class=”section-no-padding”][vc_row][vc_column][proconf_header_slider header_slider_images=”%5B%7B%22image%22%3A%22http%3A%2F%2Fworkshop.mathos.unios.hr%2Fwp-content%2Fuploads%2F2017%2F06%2FMG_0208.jpg%22%2C%22title%22%3A%22International%20Workshop%20on%20Optimal%20Control%20of%20Dynamical%20Systems%20and%20%20Applications%22%2C%22subtitle%22%3A%22One%20workshop%20that%20you%20should%20join!%22%2C%22icon%22%3A%22fa%20fa-check%22%2C%22button_text%22%3A%22REGISTER%20NOW%22%2C%22button_link%22%3A%22%23register%22%7D%2C%7B%22image%22%3A%22http%3A%2F%2Fworkshop.mathos.unios.hr%2Fwp-content%2Fuploads%2F2017%2F06%2FIMG_3182.jpg%22%2C%22title%22%3A%22International%20Workshop%20on%20Optimal%20Control%20of%20Dynamical%20Systems%20and%20%20Applications%22%2C%22subtitle%22%3A%22One%20workshop%20that%20you%20should%20join!%22%2C%22icon%22%3A%22fa%20fa-check%22%2C%22button_text%22%3A%22REGISTER%20NOW%22%2C%22button_link%22%3A%22%23register%22%7D%2C%7B%22image%22%3A%22http%3A%2F%2Fworkshop.mathos.unios.hr%2Fwp-content%2Fuploads%2F2017%2F06%2FIMG_2823.jpg%22%2C%22title%22%3A%22International%20Workshop%20on%20Optimal%20Control%20of%20Dynamical%20Systems%20and%20%20Applications%22%2C%22subtitle%22%3A%22One%20workshop%20that%20you%20should%20join!%22%2C%22icon%22%3A%22fa%20fa-check%22%2C%22button_text%22%3A%22REGISTER%20NOW%22%2C%22button_link%22%3A%22%23register%22%7D%5D”][proconf_preloader][/vc_column][/vc_row][/vc_section][vc_section overlay=”yes” overlay_type=”texture2″ bg_class=”BGlight”][vc_row][vc_column][proconf_section_title title=”We are live in” icon=”fa fa-clock-o”][proconf_countdown event_datetime=”June, 20, 2018 9:00:00″][/vc_column][/vc_row][/vc_section][vc_section bg_class=”BGprime” el_id=”event”][vc_row][vc_column width=”1/2″][proconf_eventinfo subtitle=”20 – 22 JUNE 2018″][/proconf_eventinfo][/vc_column][vc_column width=”1/2″][proconf_eventinfo icon=”fa fa-map-marker” title=”Event Location” subtitle=””]J. J. Strossmayer University of Osijek
Department of Mathematics
Trg Ljudevita Gaja 6
HR-31000 Osijek[/proconf_eventinfo][/vc_column][/vc_row][/vc_section][vc_section bg_class=”BGsecondary” el_id=”overview”][vc_row][vc_column][proconf_section_title title=”Overview and main topics” icon=”fa fa-bars”][vc_row_inner][vc_column_inner width=”1/2″][vc_wp_text]This international workshop aims at an exchange of new concepts and ideas from perspective of mathematical theory, approaches and algorithms, as well as applications of optimal control within the industry. Workshop will provide a coherent set of  invited and contributed lectures that will clarify the mathematical and applied aspects of the optimal control of dynamical systems.

With this workshop, we would like to establish cooperation between researches that come from the industry and academics. Thus, we would like to pay atention to topics that arise in applications of optimal control. In particular, applications of optimal control to robotics and mechanical systems, mechanical and electronics system design and engineering.

This workshop will have invited and contributed talks. [/vc_wp_text][/vc_column_inner][vc_column_inner width=”1/2″][vc_wp_text] TOPICS

Recent theoretical and numerical contributions in optimal control theory for finite as well as infinite dimensional problems. The topics of the workshop will include, but are not limited to:

 [/vc_wp_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][/vc_section][vc_section bg_class=”BGlight” el_id=”schedule”][vc_row][vc_column][proconf_section_title title=”EVENT SCHEDULE” icon=”fa fa-list-alt”][timeline_carousel][proconf_events_timeline date=”20″ month=”June” events=”%5B%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Opening%22%2C%22title%22%3A%22Registration%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22.%22%2C%22time%22%3A%2213%3A00-%2013%3A15%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Serkan%20Gugercin%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22Data-Driven%20Dynamical%20Modeling%20And%20Nonlinear%20Eigenvalue%20Problems%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Nulla%20hendrerit%20vitae%20nulla%20at%20ultricies.%20Suspendisse%20consequat%20tempor%20mi%2C%20eu%20tristique%20mi.%20Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Suspendisse%20dignissim%20convallis%20dolor%20at%20viverra.%20Nullam%20consequat%20nulla%20enim.%22%2C%22time%22%3A%2213%3A15%20-%2014%3A00%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Zlatko%20Drma%C4%8D%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22The%20numerics%20of%20matrix%20valued%20rational%20approximations%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Nulla%20hendrerit%20vitae%20nulla%20at%20ultricies.%20Suspendisse%20consequat%20tempor%20mi%2C%20eu%20tristique%20mi.%20Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Suspendisse%20dignissim%20convallis%20dolor%20at%20viverra.%20Nullam%20consequat%20nulla%20enim.%22%2C%22time%22%3A%2214%3A00%20-%2014%3A45%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22Vector%20Fitting%20for%20Parameterized%20Systems%22%2C%22subtitle%22%3A%22Alexander%20Grimm%22%2C%22desccription%22%3A%22Let%20%24%5C%5Cmathcal%20H(s%2C%20p)%24%20denote%20the%20transfer%20function%20of%20a%20single-input%2Fsingle-output%20parameterized%20linear%20dynamical%5Cnsystem%3B%20thus%20we%20assume%20linear%20dynamics%2C%20but%20the%20parameter%20dependency%20could%20be%20non-linear.%20We%20also%5Cnassume%20that%20we%20do%20not%20have%20access%20to%20the%20internal%20dynamics%2C%20but%20have%20access%20only%20to%20the%20input%2Foutput%5Cnmeasurements%20in%20the%20form%20of%20the%20transfer%20function%20evaluations.%20Let%5Cn%24%5Cn%5C%5C%7B%5Cn%5C%5Cxi_i%2C%5C%5Cmu_j%2C%20%5C%5Cmathcal%20H(%5C%5Cxi_i%2C%20%5C%5Cmu_j)%5Cn%5C%5C%7D_%7Bi%3D1%2Cj%3D1%7D%5E%7Bi%3Dm_s%2Cj%3Dm_p%7D%5C%5Csubset%20%5C%5CC%5C%5Ctimes%5C%5CC%5C%5Ctimes%20C%5Cn%24%5Cnbe%20the%20measurement%20data%20set%20where%20%24%5C%5C%7B%5C%5Cxi_i%5C%5C%7D%24%20and%20%24%5C%5C%7B%5C%5Cmu_j%5C%5C%7D%24%20denote%20the%20samples%20in%20the%20frequency%20(s)%20and%20parameter%5Cn(p)%20domain%2C%20respectively%3B%20and%20%24m_s%24%20and%20%24m_p%24%20are%2C%20respectively%2C%20the%20number%20of%20samples%20in%20s%20and%5Cnp.%20Then%2C%20given%20this%20measurement%20data%2C%20we%20consider%20the%20problem%20of%20finding%20a%20parametric%20dynamical%5Cnsystem%2C%20represented%20by%20its%20transfer%20function%20%24%5C%5Cmathcal%20%7B%5C%5Chat%20H%7D%5Cn(s%2C%20p)%24%2C%20that%20solves%20the%20least-squares%20problem%20(1)%5Cn%24%5Cn%5C%5Csum%5E%7Bm_s%7D_%7Bi%3D1%7D%5C%5Csum%5E%7Bm_p%7D_%7Bj%3D1%7D%20%7C%5C%5Cmathcal%20%7B%5C%5Chat%20H%7D%5Cn(%5C%5Cxi_i%2C%20%5C%5Cmu_j)-%20%5C%5Cmathcal%20H%5Cn(%5C%5Cxi_i%2C%20%5C%5Cmu_j)%7C%5E2%20%5C%5Cto%20min.%5Cn%24%5CnLet%20%24%5C%5Cmathcal%7B%5C%5Chat%20%7BH_k%7D%7D(s)%24%20denote%20the%20local%20reduced%20model%20obtained%20via%2C%20for%20example%2C%20Vector%20Fitting%20for%20the%20parameter%5Cnsample%20%24%5C%5Cmu_k%24.%20We%20combine%20the%20local%20reduced%20models%20%24%5C%5Cmathcal%7B%5C%5Chat%20%7BH_k%7D%7D(s)%24%20%20via%20coefficient%20functions%20%24%5C%5Calpha_k(p)%24%20so%20that%20the%5Cnparametric%20reduced%20model%20(2)%5Cn%24%5C%5Cmathcal%7B%5C%5Chat%20H%7D(s%2Cp)%3A%3D%5C%5Csum%5E%7Bm_p%7D_%7Bk%3D1%7D%5C%5Calpha_k(p)%5C%5Cmathcal%7B%5C%5Chat%20%7BH_k%7D%7D(s)%24%20%5Cnsolves%20the%20joint%20least-squares%20problem%20(1)%20in%20frequency%20and%20parameter.%20For%20the%20coefficients%20%24%5C%5Calpha_k(p)%24%2C%20we%5Cnconsider%20both%20the%20polynomial%20and%20rational%20parametrizations.%20In%20the%20latter%20case%2C%20we%20use%20an%20iterative%5Cnalgorithm%20based%20on%20the%20variable%20projection%20method4%20to%20automatically%20adapt%20the%20poles%20of%20the%20rational%5Cnfunctions%20%24%5C%5Calpha_k(p)%24.%20We%20compare%20our%20approach%20to%20various%20existing%20methods.%20In%20the%20case%20of%20many%20parameter%20samples%2C%20the%20order%20of%20the%20approximant%20%24%5C%5Cmathcal%20H%5Cn(s%2C%20p)%24%20could%20be%20higher%20than%5Cndesired.%20For%20those%20cases%2C%20we%20use%20a%20post-processing%20step%20that%20employs%20the%20optimal%20H2%20model%20reduction%5Cntechniques%20for%20special%20parameterizations.%20We%20demonstrate%20our%20algorithm%20on%20several%20benchmark%5Cnexamples%20with%20scalar%20and%20higher%20dimensional%20parameter%20space.%22%2C%22time%22%3A%2214%3A45%20-%2015%3A10%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-coffee%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22Coffee%20break%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22.%22%2C%22time%22%3A%2215%3A10%20-%2015%3A40%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22FEAST%20algorithm%20for%20self%20adjoint%20eigenvalue%20problems%22%2C%22subtitle%22%3A%22Luka%20Grubi%C5%A1i%C4%87%22%2C%22desccription%22%3A%22Filtered%20subspace%20iteration%20with%20Rayleigh-Ritz%20eigenvalue%5Cnextraction%20is%20a%20recently%20reviewed%20method%20in%20the%20form%20of%20the%20FEAST%5Cnalgorithm.%20In%20this%20talk%20we%20present%5Cna%20method%20motivated%20by%20the%20FEAST%20iteration%20and%20apply%20it%20directly%20on%20the%5Cnoperator%20level.%5CnThe%20core%20of%20the%20algorithm%20is%20a%20numerical%20resolvent%20calculus%20based%20on%5Cncontour%20quadratures%20coupled%20with%20discontinuous%20Petrov-Galerkin%5Cndiscretization%20of%20the%20resolvent.%20We%20show%20the%20contraction%20property%20for%20the%5Cniterative%20process%20under%20the%20assumption%20that%20the%20error%20in%20the%20approximation%5Cnof%20the%20resolvent%20is%20asymptotically%20balanced%20with%20the%20best%20approximation%5Cnrate%20for%20the%20target%20eigenfunctions%20from%20a%20given%20finite%20element%20space.%22%2C%22time%22%3A%2215%3A40%20-%2016%3A05%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22H2-optimal%20model%20order%20reduction%20of%20port-Hamiltonian%20systems%22%2C%22subtitle%22%3A%22Petar%20Mlinari%C4%87%22%2C%22desccription%22%3A%22Port-Hamiltonian%20systems%20are%20a%20type%20of%20passive%20systems%2C%20appearing%20in%20many%20domains%20of%20physics%2C%20e.g.%5Cnelectric%20circuits%20and%20mechanical%20systems.%20Since%20such%20systems%20can%20consist%20of%20many%20ordinary%20differential%5Cnequations%2C%20one%20approach%20to%20enable%20faster%20analysis%2C%20simulation%2C%20and%20control%20is%20to%20apply%20a%20model%20reduction%5Cnmethod.%20Additionally%2C%20a%20method%20preserving%20the%20port-Hamiltonian%20structure%20is%20preferred%2C%20such%20that%20the%5Cnreduced%20model%20has%20the%20same%20physical%20interpretation.%5CnFor%20unstructured%20linear%20time-invariant%20systems%2C%20the%20Iterative%20Rational%20Krylov%20Algorithm%20(IRKA)%2C%20an%5CnH2-optimal%20model%20reduction%20method%20based%20on%20interpolatory%20necessary%20optimality%20conditions%2C%20was%20introduced%5Cnby%20Antoulas%2C%20Beattie%2C%20and%20Gugercin%20%5B2%5D.%20Later%2C%20Gugercin%20et%20al.%20%5B3%5D%20proposed%20an%20interpolatory%5Cnmodel%20order%20reduction%20method%20for%20port-Hamiltonian%20systems%20inspired%20by%20IRKA.%20The%20method%20shows%5Cnits%20effectiveness%20in%20numerical%20examples%2C%20but%20it%20does%20not%20necessarily%20find%20a%20(locally)%20H2-optimal%20reduced%5Cnmodel.%20Beattie%20and%20Benner%20%5B1%5D%20derived%20interpolatory%20necessary%20optimality%20conditions%2C%20but%20did%20not%5Cnpropose%20a%20method%20to%20find%20a%20reduced%20model%20which%20would%20satisfy%20them.%5CnIn%20this%20talk%2C%20we%20will%20derive%20Gramian-based%20necessary%20optimality%20conditions%2C%20which%20take%20the%20form%20of%20a%5Cnsystem%20of%20matrix%20equations%2C%20and%20discuss%20possible%20solution%20methods%20for%20this%20system%20of%20equations.%22%2C%22time%22%3A%2216%3A05%20-%2016%3A30%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22An%20optimal%20H2%20model%20reduction%20framework%20for%20linear%20switched%20systems%22%2C%22subtitle%22%3A%22Ion%20Viktor%20Gosea%22%2C%22desccription%22%3A%22Model%20order%20reduction%20(MOR)%20is%20a%20commonly%20used%20procedure%20that%20aims%20to%20replace%20large%20and%20complex%5Cnmodels%20of%20dynamical%20processes%20with%20much%20simpler%20and%20lower%20dimensional%20models.%20This%20is%20done%20usually%5Cnto%20ease%20and%20speed%20up%20certain%20tasks%20such%20as%20control%2C%20design%2C%20and%20simulation.%5CnDynamical%20systems%20that%20are%20described%20by%20an%20interaction%20between%20continuous%20and%20discrete%20dynamics%20are%5Cnusually%20called%20hybrid%20systems.%20We%20are%20interested%20in%20the%20study%20of%20continuous-time%20systems%20with%20isolated%5Cndiscrete%20switching%20events%2C%20which%20are%20known%20as%20switched%20systems%2C%20and%20constitute%20a%20subclass%20of%20hybrid%5Cnsystems.%20More%20specifically%2C%20we%20analyze%20linear%20switched%20systems%20(LSS)%20with%20time-dependent%20switching.%5CnFor%20this%20family%20of%20systems%2C%20there%20exists%20a%20piecewise-constant%20function%20%24%5C%5Csigma%24%20(referred%20to%20as%20switching%20signal)%5Cnthat%20specifies%2C%20at%20each%20time%20instant%20t%2C%20the%20linear%20subsystem%20(or%20mode)%20which%20is%20activated.%20Additionally%2C%5Cnwe%20consider%20the%20LSS%20to%20have%20coupling%20matrices%20that%20scale%20the%20state%20variable%20after%20each%20switching%20instant.%5CnIn%20a%20sense%2C%20linear%20switched%20systems%20have%20some%20similarities%20with%20bilinear%20systems.%20For%20example%2C%20the%20LSS%5Cntime-domain%20kernels%20have%20similar%20structure%20to%20the%20bilinear%20ones%20(as%20stated%20in%20%5B1%5D).%20Moreover%2C%20in%20some%5Cnspecial%20cases%2C%20it%20is%20possible%20to%20formulate%20an%20LSS%20as%20a%20bilinear%20system%20with%20fixed%20control%20inputs.%5CnWe%20start%20by%20using%20an%20appropriate%20extension%20of%20the%20H2%20norm%20definition%20(from%20bilinear%20systems%20%5B2%2C%203%5D%5Cnto%20linear%20switched%20systems%20%5B4%5D).%20It%20can%20be%20computed%20by%20summing%20up%20inner%20products%20of%20specific%20time-%5Cndomain%20kernels%2C%20or%20equivalently%2C%20by%20using%20infinite%20controllability%20and%20observability%20Gramians%20(which%5Cnwere%20originally%20introduced%20in%20%5B1%5D).%5CnFurthermore%2C%20we%20propose%20an%20iterative%20rational%20Krylov%20algorithm%20that%20can%20be%20viewed%20as%20an%20extension%5Cnof%20B-IRKA%20%5B2%5D%20to%20the%20class%20of%20linear%20switched%20systems.%20It%20is%20based%20on%20repeatedly%20applying%20a%20two-sided%5Cnprojection%20(constructed%20in%20the%20Petrov-Galerkin%20framework)%20to%20the%20original%20LSS.%20The%20projection%20matrices%5CnVi%20and%20Wi%20are%20computed%20as%20solutions%20of%20generalized%20Sylvester%20equations%20and%20correspond%20to%20the%20mode%20i%20of%5Cnthe%20LSS.%20The%20procedure%20is%20repeated%20until%20a%20stopping%20criterion%20is%20met%20(the%20pole%20deviation%20does%20not%20exceed%5Cna%20certain%20tolerance%20value).%20Finally%2C%20we%20can%20also%20show%20that%20it%20is%20possible%20to%20derive%20similar%20optimality%5Cnconditions%20as%20the%20ones%20previously%20presented%20in%20in%20%5B2%2C%203%5D.%20The%20validity%20and%20practical%20applicability%20of%20the%5Cnproposed%20methodology%20is%20shown%20by%20means%20of%20several%20numerical%20examples.%22%2C%22time%22%3A%2216%3A30%20-%2016%3A55%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22The%20Loewner%20Framework%20between%20compression%2C%20iteration%20and%20optimal%20selection%22%2C%22subtitle%22%3A%22Dimitrios%5CtKarachalios%22%2C%22desccription%22%3A%22Model%20order%20reduction%20(MOR)%20is%20a%20tool%20used%20to%20replace%20large%20and%20complex%20models%20of%20dynamical%20processes%5Cnwith%20smaller%20and%20simpler%20dimensional%20models%2C%20which%20can%20be%20easily%20used%20for%20complete%20analysis%20such%20as%5Cncontrol%2C%20design%2C%20and%20simulation.%20The%20methods%20considered%20in%20this%20study%20are%20interpolation%20based%20and%5Cnaim%20to%20construct%20reduced-order%20models%20for%20which%20the%20corresponding%20rational%20transfer%20function%20matches%5Cnthat%20of%20the%20original%20model%20at%20selected%20interpolation%20points.%20Moreover%2C%20the%20approaches%20we%20present%20are%5Cndata-driven%2C%20in%20the%20sense%20that%20the%20required%20information%20is%20extracted%20from%20data%20(data%20points%20or%20nodes%5Cnand%20data%20measurements%20or%20function%20values)%20-%20no%20original%20model%20or%20function%20is%20required.%5CnThe%20primary%20method%20of%20this%20study%20is%20the%20Loewner%20framework%2C%20as%20introduced%20in%20see%20%5B1%5D.%20It%20produces%5Cnmodels%20directly%20from%20measurements%20in%20a%20straightforward%20manner.%20The%20main%20feature%20is%20that%20it%20provides%5Cna%20trade-off%20between%20accuracy%20of%20fit%20and%20complexity%20of%20model%2C%20by%20means%20of%20the%20singular%20value%20decay%20of%5Cnthe%20Loewner%20matrix.%5CnThe%20original%20Loewner%20framework%20is%20based%20on%20compression%20of%20the%20(usually%20large)%20data%20set%20in%20order%5Cnto%20extract%20the%20dominant%20features%20and%2C%20in%20the%20same%20time%2C%20eliminate%20the%20inherent%20redundancies.%20The%5Cnprocedure%20introduced%20in%20%5B1%5D%20relies%20on%20a%20full%20SVD%20(singular%20value%20decomposition)%20to%20compress%20the%20raw%5Cndata%20model.%20This%20might%20be%20costly%20to%20perform%2C%20especially%20for%20very%20large%20data%20sets.%5CnIn%20order%20to%20surpass%20this%20possible%20shortcoming%2C%20we%20propose%20a%20more%20e%5Cu001ecient%20approach%20which%20replaces%20the%5CnSVD%20by%20a%20CUR%20decomposition.%20In%20general%2C%20a%20CUR%20approximation%20of%20a%20given%20matrix%20M%20is%20written%20in%5Cnterms%20of%20three%20matrices%20C%2C%20U%2C%20and%20R%20such%20that%20C%20and%20R%20are%20composed%20of%20columns%20and%2C%20respectively%5Cnrows%2C%20selected%20from%20the%20ones%20of%20the%20M%20matrix.%20Finally%2C%20the%20matrix%20U%20is%20chosen%20such%20that%20product%20CUR%5Cnclosely%20approximates%20M.%20We%20propose%20a%20modified%20Loewner%20framework%2C%20in%20the%20sense%20that%20the%20interpolation%5Cnpoints%20are%20selected%20from%20the%20original%20ones%2C%20and%20not%20by%20means%20of%20compression.%20The%20indexing%20of%20the%20chosen%5Cnpoints%20coincides%20to%20the%20indexing%20of%20the%20selected%20rows%20and%20columns%20of%20the%20Loewner%20matrix.%20The%20selection%5Cnalgorithm%20is%20based%20on%20the%20CUR-DEIM%20approach%20in%20%5B4%5D.%5CnWe%20also%20studied%20the%20AAA%20algorithm%2C%20as%20introduced%20in%20%5B2%5D.%20It%20can%20be%20viewed%20as%20an%20adaptive%20and%20iterative%5Cnversion%20of%20the%20Loewner%20framework%20and%20aims%20to%20optimize%20the%20approximation%20error%20by%20means%20of%20a%20least%5Cnsquares%20approach.%20The%20order%20of%20the%20rational%20approximant%20is%20increased%20after%20each%20step%20for%20better%20accuracy.%5CnWe%20managed%20to%20construct%20low%20order%20models%20for%20physical%20problems%20such%20as%20Euler-Bernoulli%20clamped%5Cnbeam%20model%20and%201D%20heat%20diffusion%20model.%20Additionally%2C%20we%20tested%20the%20methods%20for%20artificial%20examples%5Cnwhich%20commonly%20appear%20in%20approximation%20theory%2C%20such%20as%20the%20Bessel%20function%2C%20the%20hyperbolic%20sine%5Cnfunction%2C%20and%20the%20Heaviside%20step%20function.%5CnAlthough%20a%20direct%20method%2C%20the%20original%20Loewner%20framework%20in%20%5B1%5D%20proved%20to%20be%20comparable%20(in%20terms%5Cnof%20approximation%20quality)%20to%20the%20other%20methods%20which%20rely%20on%20optimization%20tools.%20Moreover%2C%20in%20the%5Cncase%20of%20the%20Heaviside%20function%2C%20we%20were%20able%20to%20find%20rational%20functions%20via%20the%20Loewner%20framework%2C%5Cnwhich%20turned%20out%20to%20be%20very%20close%20to%20the%20optimal%20minimax%20rational%20function%20(which%20is%20the%20solution%20of%5Cnthe%20fourth%20Zolotarev%20problem%20%5B3%5D).%22%2C%22time%22%3A%2216%3A55%20-%2017%3A20%20%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-cutlery%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22Workshop%20dinner%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22Enjoy!%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5Cn%5Cn%22%2C%22time%22%3A%2218%3A30%22%2C%22footer_desc%22%3A%22Venue%3A%20Hotel%20Osijek%2C%5Cn%C5%A0ama%C4%8Dka%204%2C%2031000%20Osijek%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%5D”][proconf_events_timeline date=”21″ month=”June” events=”%5B%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Edin%20Ko%C4%8Do%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22Hybrid%20Compliance%20Control%20for%20a%20Bioinspired%20Quadruped%20Robot%22%2C%22title%22%3A%22To%20be%20announced%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22To%20be%20added.%22%2C%22time%22%3A%229%3A00%20-%209%3A45%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Andrej%20Joki%C4%87%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22On%20Structure%20and%20Trade-offs%20in%20Analysis%20and%20Control%20of%20Large-scale%20Dynamical%20Networks%22%2C%22title%22%3A%22To%20be%20announced%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22To%20be%20added.%22%2C%22time%22%3A%229%3A45%20-%2010%3A30%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22International%20Workshop%20on%20Optimal%20Control%20of%20Dynamical%20Systems%20and%20%20Applications%22%2C%22title%22%3A%22Fast%20Optimal%20Damping%22%2C%22subtitle%22%3A%22Ivan%20Slapni%C4%8Dar%22%2C%22desccription%22%3A%22We%20formulate%20the%20eigenvalue%20problem%20underlying%20the%20mathematical%20model%20of%20a%20linear%20vibrational%20system%20as%20an%20eigenvalue%20problem%20of%20a%20diagonal-plus-rank-one%20matrix%20%24A%24.%20The%20eigenvector%20matrix%20of%20%24A%24%20has%20Cauchy-like%20structure.%20We%20compute%20the%20trace%20of%20the%20solution%20of%20the%20Lyapunov%20equation%20%24AX%2BXA%5E*%3D-GG%5E*%24%20using%20fast%20computations%20with%20Cauchy-like%20and%20Toeplitz-like%20matrices.%20Here%20%24G%24%20is%20a%20low-rank%20matrix%20which%20depends%20on%20the%20damped%20eigenfrequencies.%20This%20is%20joint%20work%20with%20Nevena%20Jakov%C4%8Devi%C4%87%20Stor.%22%2C%22time%22%3A%2210%3A30%20-%2010%3A55%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-coffee%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22Coffee%20break%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22.%22%2C%22time%22%3A%2210%3A55%20-%2011%3A25%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Hybrid%20Compliance%20Control%20for%20a%20Bioinspired%20Quadruped%20Robot%22%2C%22title%22%3A%22Balanced%20Truncation%20Model%20Reduction%20for%20Systems%20with%20Nonzero%20Initial%20Condition%22%2C%22subtitle%22%3A%22Matthias%20Voigt%22%2C%22desccription%22%3A%22Balanced%20truncation%20is%20one%20of%20the%20most%20established%20methods%20for%20model%20reduction%20of%20linear%20time-invariant%5Cnsystems.%20However%2C%20this%20method%20may%20give%20reduced%20models%20that%20result%20in%20large%20errors%20if%20the%20initial%20value%5Cnis%20not%20equal%20to%20zero.%20We%20propose%20a%20new%20way%20of%20reformulating%20the%20system%20by%20shifting%20the%20state%20by%5Cnan%20L2-function%20and%20extending%20the%20input%20vector.%20Than%20classical%20balanced%20truncation%20can%20be%20applied%5Cnand%20a%20parameter-dependent%20error%20bound%20is%20obtained.%20We%20show%20how%20reduced-order%20models%20can%20be%5Cnpractically%20constructed%20and%20how%20to%20eficiently%20determine%20an%20optimal%20error%20bound.%20We%20conclude%20the%20talk%5Cnwith%20numerical%20experiments%20and%20a%20comparison%20to%20other%20approaches.%20This%20is%20joint%20work%20with%20Christian%5CnSchr%C3%B6der.%22%2C%22time%22%3A%2211%3A25%20-%2011%3A50%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Hybrid%20Compliance%20Control%20for%20a%20Bioinspired%20Quadruped%20Robot%22%2C%22title%22%3A%22On%20implicit%20restarting%20of%20Second%20Order%20Arnoldi%20procedure%20for%20quadratic%20eigenvalue%20problem%22%2C%22subtitle%22%3A%22Ivana%20%C5%A0ain%20Glibi%C4%87%22%2C%22desccription%22%3A%22Quadratic%20eigenvalue%20problem%20(QEP)%20is%20often%20solved%20by%20linearizing%20and%20then%20deploying%20well%20known%5Cntechniques%20to%20solve%20the%20resulting%20linear%20(generalized)%20eigenproblem.%20However%2C%20a%20generic%20linear%20eigensolver%5Cnis%20unaware%20of%20the%20underlying%20structure%20of%20the%20quadratic%20problem%2C%20which%20may%20cause%20loss%20of%20important%5Cnstructural%20spectral%20properties%20of%20the%20original%20problem.%5CnBai%20and%20Su%20(2005.)%20first%20realized%20that%20in%20the%20case%20of%20iterative%20Arnoldi-type%20methods%2C%20it%20is%20advantageous%5Cnto%20apply%20the%20Rayleigh-Ritz%20projection%20directly%20to%20the%20initial%20QEP.%20To%20that%20end%2C%20they%20introduced%5Cnsecond%20order%20Krylov%20subspaces%2C%20and%20the%20corresponding%20second%20order%20Arnoldi%20procedure%20for%20generating%5Cnorthonormal%20bases.%20The%20resulting%20method%2C%20Second%20Order%20Arnoldi%20(SOAR)%2C%20is%20further%20modified%20yielding%5CnTOAR%20(Lu%2C%20Su%20and%20Bai%2C%202016).%5CnSOAR%20procedure%20is%20also%20used%20for%20dimension%20reduction%20of%20large%20scale%20second%20order%20dynamical%20systems.%5CnThe%20key%20feature%20of%20this%20approach%20is%20perseverance%20of%20the%20structure%20of%20the%20dynamical%20system.%5CnIn%20this%20talk%2C%20we%20will%20present%20implicit%20restarting%20in%20SOAR(TOAR)%20in%20context%20of%20computing%20the%20prescribed%5Cnnumber%20of%20eigenvalues%20of%20QEP.%20The%20wanted%20number%20of%20eigenvalues%20is%20much%20smaller%20than%20the%20dimension%5Cnof%20the%20original%20problem.%20The%20emphasize%20of%20the%20talk%20will%20be%20the%20issues%20of%20better%20choices%20of%20starting%5Cnvectors%2C%20choosing%20shifts%20to%20construct%20polynomial%20filters%20during%20the%20restart%20process%2C%20and%20extracting%20the%5Cnwanted%20eigenvalues%20and%20eigenvectors%2C%20with%20particular%20attention%20to%20the%20peculiarities%20of%20the%20quadratic%5Cnproblem.%22%2C%22time%22%3A%2211%3A50%20-%2012%3A15%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-cutlery%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Hybrid%20Compliance%20Control%20for%20a%20Bioinspired%20Quadruped%20Robot%22%2C%22title%22%3A%22Lunch%20break%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22.%22%2C%22time%22%3A%2212%3A15%20-%2014%3A00%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Peter%20Benner%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22Riccati-Based%20Feedback%20Control%20of%20Nonlinear%20Unsteady%20PDEs%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Nulla%20hendrerit%20vitae%20nulla%20at%20ultricies.%20Suspendisse%20consequat%20tempor%20mi%2C%20eu%20tristique%20mi.%20Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Suspendisse%20dignissim%20convallis%20dolor%20at%20viverra.%20Nullam%20consequat%20nulla%20enim.%22%2C%22time%22%3A%2214%3A00%20-%2014%3A45%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22New%20Gramians%20for%20Linear%20Switched%20Systems%3A%20Reachability%2C%20Observability%2C%20and%20Model%20Reduction%22%2C%22subtitle%22%3A%22Igor%20Pontes%20Duff%22%2C%22desccription%22%3A%22Balanced%20truncation%20is%20one%20of%20the%20most%20common%20model%20order%20reduction%20techniques.%20For%20linear%20dynamical%20systems%2C%20it%20relies%20on%20the%20computation%20of%20reachability%20and%20observability%20Gramians%2C%20which%20are%20the%20solution%20%24X%24%20of%20algebraic%20Lyapunov%20equations%20as%5Cn%24AX%2BXA%5ET%2BQ%20%3D%200.%24%5CnLater%2C%20the%20concept%20of%20algebraic%20Gramians%20was%20extended%20to%20bilinear%20systems.%20In%20this%20context%2C%20they%20satisfy%20generalized%20Lyapunov%20equations%20as%20%20%5Cn%24%20AX%2BXA%5ET%2B%20%5C%5Csum_%7Bj%3D1%7D%5EM%20D_jXD_j%5ET%20%2BQ%20%3D%200.%20%24%5CnAs%20a%20result%2C%20these%20algebraic%20Gramians%20allow%20us%20to%20compute%20reduced-order%20systems%20for%20bilinear%20systems.%5CnIn%20this%20talk%2C%20an%20extension%20of%20balanced%20truncation%20for%20model%20reduction%20of%20continuous-time%20%20Linear%20Switched%20Systems%20(LSS)%20is%20proposed.%20%5CnFirst%2C%20we%20recast%20a%20linear%20%20switched%20system%20as%20a%20bilinear%20system.%20Then%2C%20inspired%20by%20the%20bilinear%20theory%2C%20we%20propose%20algebraic%20Gramians%20for%20LSS%2C%20which%20satisfy%20generalized%20Lyapunov%20equations.%20Also%2C%20we%20prove%20that%20these%20Gramians%20encode%20the%20reachability%20and%20observability%20sets.%20%20This%20allows%20us%20to%20find%20those%20states%20that%20are%20hard%20to%20reach%20and%20hard%20to%20observe%20via%20an%20appropriate%20transformation.%20Truncating%20such%20states%20yields%20reduced-order%20systems.%20The%20efficiency%20of%20these%20approximations%20is%2C%20then%2C%20demonstrated%20in%20some%20numerical%20examples.%22%2C%22time%22%3A%2214%3A45%20-%2015%3A10%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22Greedy%20optimal%20control%20for%20elliptic%20problems%20and%20its%20application%20to%20turnpike%20problems%22%2C%22subtitle%22%3A%22Martin%20Lazar%22%2C%22desccription%22%3A%22In%20this%20talk%20we%20deal%20with%20the%20approximation%20of%20optimal%20controls%20for%20parameter-dependent%20elliptic%20and%5Cnparabolic%20equations.%20We%20adapt%20well-known%20results%20on%20greedy%20algorithms%20to%20approximate%20in%20an%20efficient%5Cnway%20the%20optimal%20controls%20for%20parametrized%20elliptic%20control%20problems.%20Our%20results%20yield%20an%20optimal%5Cnapproximation%20procedure%20that%2C%20in%20particular%2C%20performs%20better%20than%20simply%20sampling%20the%20parameter-%5Cnspace%20to%20compute%20controls%20for%20each%20parameter%20value.%20Generalisation%20of%20the%20method%20to%20parabolic%20control%5Cnproblems%20would%20lead%20to%20greedy%20selections%20of%20the%20parameters%20that%20depend%20on%20the%20initial%20datum%20under%5Cnconsideration.%20To%20avoid%20this%20dificulty%20we%20employ%20the%20turnpike%20property%20for%20time%20evolution%20control%5Cnproblems%20that%20ensures%20the%20asymptotic%20simplification%20of%20optimal%20control%20problems%20for%20evolution%20equations%5Cntowards%20the%20elliptic%20steady-state%20ones%20in%20long%20time%20horizons%20%5B0%2C%20T%5D.%20The%20combination%20of%20the%20turnpike%5Cnproperty%20and%20greedy%20methods%20allows%20us%20to%20develop%20eficient%20methods%20for%20the%20approximation%20of%20the%5Cnparameter-dependent%20parabolic%20optimals%20too.%20We%20present%20various%20numerical%20experiments%20discussing%5Cnthe%20efficiency%20of%20our%20methodology%20and%20its%20application%20to%20turnpike%20control%20problems.%22%2C%22time%22%3A%2215%3A10%20-%2015%3A35%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-coffee%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22Coffee%20break%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22.%22%2C%22time%22%3A%2215%3A35%20-%2016%3A05%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22Optimality%20criteria%20method%20for%20optimal%20design%20problems%22%2C%22subtitle%22%3A%22Ivana%20Crnjac%22%2C%22desccription%22%3A%22In%20optimal%20design%20problems%20the%20goal%20is%20to%20find%20the%20arrangement%20of%20given%20materials%20within%20the%20body%20which%5Cnoptimizes%20its%20properties%20with%20respect%20to%20some%20optimality%20criteria.%20The%20performance%20of%20the%20mixture%5Cnis%20usually%20measured%20by%20an%20integral%20functional%2C%20while%20optimality%20of%20the%20mixture%20is%20achieved%20through%5Cnminimization%20or%20maximization%20of%20this%20functional%2C%20under%20constraints%20on%20amount%20of%20materials%20and%20PDE%5Cnconstraints%20that%20underlay%20involved%20physics.%5CnWe%20consider%20multiple-state%20optimal%20design%20problems%20from%20conductivity%20point%20of%20view%2C%20where%20thermal%20(or%5Cnelectrical)%20conductivity%20is%20modeled%20with%20stationary%20diffusion%20equation%20and%20restrict%20ourselves%20to%20domains%5Cnfilled%20with%20two%20isotropic%20materials.%20Since%20the%20classical%20solution%20usually%20does%20not%20exist%2C%20we%20use%20relaxation%5Cnby%20the%20homogenization%20method%20%5B2%5D%20in%20order%20to%20get%20a%20proper%20relaxation%20of%20the%20original%20problem.%5CnOne%20of%20numerical%20methods%20used%20for%20solving%20these%20problems%20is%20the%20optimality%20criteria%20method%2C%20an%20iterative%5Cnmethod%20based%20on%20optimality%20conditions%20of%20the%20relaxed%20formulation.%20In%20the%20case%20of%20a%20single-state%20problem%5Cnthis%20method%20is%20described%20in%20%5B1%5D%2C%20where%20it%20is%20also%20proved%20that%20it%20converges%20in%20the%20case%20of%20a%20self-adjoint%5Cnoptimization%20problems.%20Based%20on%20the%20optimality%20conditions%20derived%20in%20%5B1%5D%2C%20a%20variant%20of%20optimality%20criteria%5Cnmethod%20for%20multiple-state%20problems%20was%20introduced%20in%20%5B3%5D%20.%20It%20appears%20that%20this%20variant%20works%20properly%5Cnfor%20maximization%20of%20conic%20sum%20of%20energies%2C%20but%20fails%20for%20the%20minimization%20of%20the%20same%20functional.%5CnWe%20rewrite%20optimality%20conditions%20for%20relaxed%20problem%20and%20develop%20a%20variant%20of%20optimality%20criteria%5Cnmethod%20suitable%20for%20energy%20minimization%20problems.%20We%20also%20prove%20convergence%20of%20this%20method%20in%20a%5Cnspecial%20case%20when%20a%20number%20of%20states%20is%20less%20then%20the%20space%20dimension%20and%20in%20the%20spherically%20symmetric%5Cncase.%20Presented%20method%20can%20be%20adapted%20to%20similar%20problems%20in%20the%20context%20of%20linearized%20elasticity.%22%2C%22time%22%3A%2216%3A05%20-%2016%3A30%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22Composite%20elastic%20plate%20via%20general%20homogenization%20theory%22%2C%22subtitle%22%3A%22Jelena%20Jankov%22%2C%22desccription%22%3A%22General%2C%20non-periodic%20homogenization%20theory%20is%20well%20developed%20for%20second%20order%20elliptic%20partial%20differ-%5Cnential%20equations%2C%20where%20the%20key%20role%20plays%20the%20notion%20of%20H-convergence.%20It%20was%20introduced%20by%20Spagnolo%5Cnthrough%20the%20concept%20of%20G-convergence%20(1968)%20for%20the%20symmetric%20case%2C%20and%20further%20generalized%20by%20Tartar%5Cn(1975)%20and%20Murat%20and%20Tartar%20(1978)%20for%20non-symmetric%20coeficients%20under%20the%20name%20H-convergence.%5CnSome%20aspects%20for%20higher%20order%20elliptic%20problems%20were%20also%20considered%20by%20Zhikov%2C%20Kozlov%2C%20Oleinik%20and%5CnNgoan%20(1979).%20Homogenization%20theory%20is%20probably%20the%20most%20successful%20approach%20for%20dealing%20with%20optimal%20design%20problems%20(in%20conductivity%20or%20linearized%20elasticity)%2C%20that%20consists%20in%20arranging%20given%20materials%5Cnsuch%20that%20obtained%20body%20satisfies%20some%20optimality%20criteria%2C%20which%20is%20mathematically%20usually%20expressed%5Cnas%20minimization%20of%20some%20(integral)%20functional%20under%20some%20(PDE)%20constrains.%5CnMotivated%20by%20a%20possible%20application%20of%20the%20homogenization%20theory%20in%20optimal%20design%20problems%20for%20elastic%5Cnplates%2C%20we%20adapt%20the%20general%20homogenization%20theory%20for%20Kirchoff-Love%20elastic%20plate%20equation%2C%20which%20is%20a%5Cnfourth%20order%20elliptic%20equation.%20In%20addition%20to%20the%20compactness%20result%2C%20we%20prove%20a%20number%20of%20properties%5Cnof%20H-convergence%2C%20such%20as%20locality%2C%20irrelevance%20of%20the%20boundary%20conditions%2C%20corrector%20results%2C%20etc.%20Using%5Cnthis%20newly%20developed%20theory%2C%20we%20derive%20expressions%20for%20elastic%20coeficients%20of%20composite%20plate%20obtained%5Cnby%20mixing%20two%20materials%20in%20thin%20layers%20(known%20as%20laminated%20materials)%2C%20and%20for%20mixing%20two%20materials%5Cnin%20low-contrast%20regime.%20Moreover%2C%20we%20also%20derive%20optimal%20bounds%20on%20the%20effective%20energy%20of%20a%20composite%5Cnmaterial%2C%20known%20as%20Hashin-Shtrikman%20bounds.%22%2C%22time%22%3A%2216%3A30%20-%2016%3A55%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22Small%20Amplitude%20Homogenization%22%2C%22subtitle%22%3A%22Irena%20Brdar%22%2C%22desccription%22%3A%22We%20analyse%20an%20optimal%20design%20problem%20for%20the%20stationary%20diffusion%20equation.%20The%20aim%20is%20to%20find%20an%5Cnoptimal%20arrangement%20of%20two%20materials%20minimising%20an%20objective%20function%20whereby%20the%20proportion%20of%20the%5Cnmaterials%20is%20fixed%20and%20predetermined.%20Optimal%20design%20problem%20is%20ill-posed%20in%20the%20sense%20that%20it%20doesn’t%5Cnadmit%20a%20minimizer%20in%20general.%20Therefore%20it%20is%20relaxed%20by%20using%20the%20assumption%20of%20small%20amplitude%20and%5CnH-measures.%20Generalisation%20of%20the%20method%20to%20higher%20order%20asymptotic%20and%20time%20dependent%20problems%5Cnwill%20be%20discussed.%20Numerical%20examples%20will%20be%20presented%20as%20well.%22%2C%22time%22%3A%2216%3A55%20-%2017%3A20%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%5D”][proconf_events_timeline date=”22″ month=”June” events=”%5B%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22Inducing%20passivity%20in%20data-driven%20models%22%2C%22title%22%3A%22To%20be%20announced%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Nulla%20hendrerit%20vitae%20nulla%20at%20ultricies.%20Suspendisse%20consequat%20tempor%20mi%2C%20eu%20tristique%20mi.%20Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Suspendisse%20dignissim%20convallis%20dolor%20at%20viverra.%20Nullam%20consequat%20nulla%20enim.%22%2C%22time%22%3A%229%3A00%20-%209%3A45%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22speaker%22%2C%22speaker%22%3A%22Ninoslav%20Truhar%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22event%22%2C%22event%22%3A%22Damping%20optimization%20and%20bounds%20on%20eigenspaces%22%2C%22title%22%3A%22To%20be%20announced%22%2C%22subtitle%22%3A%22%7BServed%20By%3A%7D%20Spicehub%22%2C%22desccription%22%3A%22Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Nulla%20hendrerit%20vitae%20nulla%20at%20ultricies.%20Suspendisse%20consequat%20tempor%20mi%2C%20eu%20tristique%20mi.%20Lorem%20ipsum%20dolor%20sit%20amet%2C%20consectetur%20adipiscing%20elit.%20Suspendisse%20dignissim%20convallis%20dolor%20at%20viverra.%20Nullam%20consequat%20nulla%20enim.%22%2C%22time%22%3A%229%3A45%20-%2010%3A30%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22The%20numerics%20of%20matrix%20valued%20rational%20approximations%22%2C%22title%22%3A%22Optimal%20passive%20control%20of%20vibrational%20systems%20using%20mixed%20performance%20measures%22%2C%22subtitle%22%3A%22Ivica%20Naki%C4%87%22%2C%22desccription%22%3A%22We%20will%20present%20new%20performance%20measures%20for%20vibrational%20systems%20based%20on%20the%20H2%20norm%20of%20linear%5Cncontrol%20systems.%20Examples%2C%20both%20theoretical%20and%20concrete%2C%20will%20be%20given%20showing%20how%20these%20perfor-%5Cnmance%20measures%20stack%20up%20against%20standard%20ones%20when%20used%20as%20an%20optimization%20criterion%20for%20the%20optimal%5Cndamping%20of%20vibrational%20systems.%22%2C%22time%22%3A%2210.30%20-%2010%3A55%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-coffee%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22The%20numerics%20of%20matrix%20valued%20rational%20approximations%22%2C%22title%22%3A%22Coffee%20break%22%2C%22subtitle%22%3A%22%7B%7D%22%2C%22desccription%22%3A%22.%22%2C%22time%22%3A%2210%3A55%20-%2011%3A25%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22The%20numerics%20of%20matrix%20valued%20rational%20approximations%22%2C%22title%22%3A%22Sampling-free%20parametric%20model%20reduction%20of%20structured%20systems%22%2C%22subtitle%22%3A%22Zoran%20Tomljanovi%C4%87%22%2C%22desccription%22%3A%22We%20consider%20a%20%20parametric%20%20linear%20time%20invariant%20dynamical%20systems%20represented%20%20as%5Cn%5C%5Cbegin%7Balign*%7D%5Cn%20E%20%20%5C%5Cdot%20x(t)%20%26%3D%20A(p)x(t)%20%2B%20Bu(t)%2C%5C%5C%5C%5C%5Cny(t)%20%26%3D%20Cx(t)%2C%5Cn%5C%5Cend%7Balign*%7D%5Cnwhere%20%24%20E%2C%20A(p)%20%5C%5Cin%20%5C%5Cmathbb%7BR%7D%5E%7Bn%5C%5Ctimes%20n%7D%24%2C%20%24B%5C%5Cin%20%5C%5Cmathbb%7BR%7D%5E%7Bn%5C%5Ctimes%20m%7D%20%24%20and%20%24C%5C%5Cin%20%5C%5Cmathbb%7BR%7D%5E%7Bl%5C%5Ctimes%20n%7D%24.%20We%20assume%20that%20%24A(p)%24%20depends%20on%20%24k%5C%5Cll%20n%24%20parameters%20%24p%3D(p_1%2C%20p_2%2C%20%5C%5Cldots%2C%20p_k)%24%20such%20that%5Cn%24A(p)%3DA_0%2BU%5C%5C%2C%20%5C%5Crm%7Bdiag%7D%20(p_1%2C%20p_2%2C%20%5C%5Cldots%2C%20p_k)V%5ET%2C%24%5Cnwhere%20%20%24U%2C%20V%20%5C%5Cin%20%5C%5Cmathbb%7BR%7D%5E%7Bn%5C%5Ctimes%20k%7D%24%20are%20given%20fixed%20matrices.%5Cn%5Cn%5CnWe%20propose%20an%20approach%20for%20approximating%20the%20full-order%20transfer%20function%20%20with%20a%20reduced-order%20model%20that%20retains%20the%20structure%20of%20parametric%20dependence%5Cnand%20(typically)%20offers%20uniformly%20high%20fidelity%20across%20the%20full%20parameter%20range.%20%20Remarkably%2C%20the%20proposed%20reduction%5Cnprocess%20removes%20the%20need%20for%20parameter%20sampling%20and%20thus%20does%20not%20depend%20on%20identifying%20particular%20parameter%20values%20of%20interest.%20%20In%20our%20approach%20%20the%20%20Sherman-Morrison-Woodbury%20formula%20%20allows%20us%20to%20construct%20a%20parameterized%20reduced%20order%20model%20from%20transfer%20functions%20of%20four%20subsystems%20that%20do%20not%20depend%20on%20parameters.%20In%20this%20form%20%20%20one%20can%20apply%20well-established%20model%20reduction%20techniques%20for%20non-parametric%20systems.%5CnThe%20overall%20process%20is%20well%20suited%20for%20computationally%20efficient%20parameter%20optimization%20and%20the%20study%20of%20important%20system%20properties.%5Cn%5Cn%5CnOne%20of%20the%20main%20applications%20of%20our%20approach%20is%20for%20damping%20optimization%20where%20parameters%20represent%20viscosities.%20The%20main%20problem%20is%20to%20determine%20the%20best%20damping%20matrix%20that%20is%20able%20to%20minimize%20influence%20of%20the%20disturbances%2C%20%20on%20the%20output%20of%20the%20system.%5CnWe%20use%20a%20minimization%20criteria%20based%20on%20the%20%24%5C%5Cmathcal%7BH%7D_2%24%20%20system%20norm.%5CnIn%20realistic%20settings%2C%20damping%20optimization%20is%20a%20very%20demanding%20problem.%5CnWe%20find%20that%20the%20parametric%20model%20reduction%20approach%20described%20here%20offers%20a%20new%20tool%20with%20significant%20advantages%20for%20the%20efficient%20optimization%20of%20damping%20in%20such%20problems.%5Cn%5CnThis%20is%20joint%20work%20with%20Christopher%20Beattie%20and%20Serkan%20Gugercin%20from%20Virginia%20Tech%2C%20Blacksburg%2C%20USA.%22%2C%22time%22%3A%2211%3A25%20-%2011%3A50%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22The%20numerics%20of%20matrix%20valued%20rational%20approximations%22%2C%22title%22%3A%22Perturbation%20Bounds%20for%20Parameter%20Dependent%20Quadratic%20Eigenvalue%20Problem%22%2C%22subtitle%22%3A%22Matea%20Puva%C4%8Da%22%2C%22desccription%22%3A%22We%20consider%20a%20quadratic%20eigenvalue%20problem%20(QEP)%3A%5Cn%24%24%5Cn(%5C%5Clambda%5E2(v)M(v)%2B%5C%5Clambda(v)%20D(v)%20%2B%20K(v))x(v)%3D0%2C%5Cn%24%24%5Cnwhere%20matrices%20%24M%24%20and%20%24K%24%20are%20Hermitian%20semidefinite%20and%20at%20least%20one%20of%20them%20is%20positive%20definite%2C%20and%20also%20%24M%2C%20D%24%20and%20%24K%24%20depend%20on%20%24v%3D%5Bv_1%2C%5C%5Cdots%2C%20v_s%5D%5C%5Cin%20%5C%5Cmathbb%7BR%7D%5Es%24.%5Cn%5CnThe%20most%20widely%20used%20approach%20for%20solving%20the%20polynomial%20(which%20includes%20QEP)%20eigenvalue%20problem%20is%20to%20linearize%20in%20order%20to%20produce%20a%20larger%20order%20pencil%2C%20whose%20eigensystem%20can%20be%20found%20by%20any%20method%20for%20generalized%20eigenproblems.%20%5Cn%5CnTo%20avoid%20lineatization%2C%20we%20propose%20two%20different%20types%20of%20bounds%2C%20%20the%20first%20is%20a%20simple%20first%20order%20approximation%20of%20function%20of%20several%20variables%20while%20the%20second%20one%20considers%20structured%20perturbation.%5Cn%5CnThe%20first%20bound%20is%20the%20upper%20bound%20for%20the%20first%20order%20approximation%2C%20based%20on%20Taylor’s%20theorem%2C%20for%20the%20eigenvalues%20and%20the%20corresponding%20left%20and%20right%20eigenvectors%20of%20the%20QEP.%5Cn%5CnThe%20second%20bound%20contains%20bound%20for%20relative%20gaps%20that%20arise%20in%20perturbation%20theory.%5Cn%20This%20is%20joint%20work%20with%20Ninoslav%20Truhar%20and%20Zoran%20Tomljanovi%C4%87.%22%2C%22time%22%3A%2211%3A50%20-%2012%3A15%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%2C%7B%22left_side%22%3A%22custom%22%2C%22speaker%22%3A%22Christopher%20Beattie%22%2C%22icon%22%3A%22fa%20fa-microphone%22%2C%22type%22%3A%22custom%22%2C%22event%22%3A%22The%20numerics%20of%20matrix%20valued%20rational%20approximations%22%2C%22title%22%3A%22Relative%20perturbation%20bounds%20for%20quadratic%20eigenvalue%20problem%22%2C%22subtitle%22%3A%22Suzana%20Miodragovi%C4%87%22%2C%22desccription%22%3A%22We%20present%20new%20relative%20perturbation%20bounds%20for%20the%20eigenvalues%20and%20eigensubspaces%20for%20quadratic%20eigenvalue%20problem%20%24%5C%5Clambda%5E2%20M%20x%20%2B%20%5C%5Clambda%20C%20x%20%2B%20Kx%3D0%24%2C%20where%20%24M%24%20and%20%24K%24%20are%20nonsingular%20Hermitian%20and%20%24C%24%20is%20any%20Hermitian%20matrix.%20First%2C%20we%20derive%20relative%20perturbation%20bounds%20for%20the%20eigenvalues%20and%20the%20%24%5C%5Csin%20%5C%5CTheta%24%20type%20theorems%20for%20the%20eigensubspaces%20of%20the%20regular%20matrix%20pairs%20%24(A%2CB)%24%2C%20where%20both%20%24A%24%20and%20%24B%24%20are%20Hermitian%20matrices.%20Using%20a%20proper%20linearization%20and%20new%20relative%20perturbation%20bounds%20for%20regular%20matrix%20pairs%20%24(A%2CB)%24%2C%20we%20develop%20corresponding%20relative%20perturbation%20bounds%20for%20the%20eigenvalues%20and%20the%20%24%5C%5Csin%20%5C%5CTheta%24%20type%20theorems%20for%20the%20eigensubspaces%20for%20the%20considered%20regular%20quadratic%20eigenvalue%20problem.%20Our%20bound%20can%20be%20applied%20to%20the%20gyroscopic%20systems%20which%20will%20be%20also%20shown.%20The%20obtained%20bounds%20will%20be%20illustrated%20by%20numerical%20examples.%22%2C%22time%22%3A%2212%3A15%20-%2012%3A40%20%22%2C%22icon_bg_color%22%3A%22default-bg%22%2C%22right_bg_color%22%3A%22default-bg%22%7D%5D”][/timeline_carousel][/vc_column][/vc_row][/vc_section][vc_section overlay=”yes” overlay_type=”texture1″ bg_class=”BGsecondary” el_id=”speakers”][vc_row][vc_column][proconf_section_title title=”Confirmed invited speakers” icon=”fa fa-microphone”][proconf_speakers display=”specific” speakers=”%5B%7B%22speaker%22%3A%22548%22%7D%2C%7B%22speaker%22%3A%22547%22%7D%2C%7B%22speaker%22%3A%22545%22%7D%2C%7B%22speaker%22%3A%22543%22%7D%2C%7B%22speaker%22%3A%22394%22%7D%5D” css_animation=”none”][/vc_column][/vc_row][/vc_section][vc_section][vc_row][vc_column][proconf_section_title title=”Invited lectures” icon=”fa fa-lightbulb-o”][/vc_column][/vc_row][vc_row][vc_column width=”1/2″][vc_tta_accordion active_section=”5″ collapsible_all=”true”][vc_tta_section title=”Christopher Beattie” tab_id=”1513964574667-35334c0c-22121″][vc_column_text]“Inducing passivity in data-driven models”[/vc_column_text][/vc_tta_section][vc_tta_section title=”Peter Benner” tab_id=”1513964574669-87e3fe4b-12222″][vc_column_text]“Riccati-Based Feedback Control of Nonlinear Unsteady PDEs”[/vc_column_text][/vc_tta_section][vc_tta_section title=”Zlatko Drmač” tab_id=”1513964574672-baf7c538-12223″][vc_column_text]“The numerics of matrix valued rational approximations”[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][vc_column width=”1/2″][vc_tta_accordion active_section=”5″ collapsible_all=”true”][vc_tta_section title=”Serkan Gugercin” tab_id=”1513964574676-ed95bc43-22124″][vc_column_text]Data-driven dynamical modeling and nonlinear eigenvalue problems[/vc_column_text][/vc_tta_section][vc_tta_section title=”Andrej Jokić” tab_id=”1518087826193-9b06b823-f9d8″][vc_column_text]“On Structure and Trade-offs in Analysis and Control of Large-scale Dynamical Networks”[/vc_column_text][/vc_tta_section][vc_tta_section title=”Edin Kočo” tab_id=”1518087876390-e74711c7-fc21″][vc_column_text]“Hybrid Compliance Control for a Bioinspired Quadruped Robot”[/vc_column_text][/vc_tta_section][vc_tta_section title=”Ninoslav Truhar” tab_id=”1513964574677-dcf85883-22125″][vc_column_text]“Damping optimization and bounds on eigenspaces”[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][/vc_row][/vc_section][vc_section el_id=”datesandinformation”][vc_row][vc_column][proconf_section_title title=”Important dates and information” icon=”fa fa-info-circle”][/vc_column][/vc_row][vc_row][vc_column width=”1/2″][vc_tta_accordion active_section=”5″ collapsible_all=”true”][vc_tta_section title=”Important dates” tab_id=”1497093509645-e44de209-2013″][vc_column_text]

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Abstract submision” tab_id=”1497093509753-0ef89526-8930″][vc_column_text]

Url to abstract file can be submited during the registration process or alternatively it can be sent to email workshop18@mathos.hr. [/vc_column_text][/vc_tta_section][vc_tta_section title=”About Osijek and accomodation” tab_id=”1497093509870-42dfff21-d04f”][vc_column_text]Workshop will be held in Osijek. Osijek is the largest city in the eastern part of Croatia, known as Slavonia and Baranja. It is situated on the right bank of the Drava River. The city is an administrative, industrial, cultural and university center of eastern Croatia.

Accommodation

Osijek info: Tourist board of the city of Osijek. [/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][vc_column width=”1/2″][vc_tta_accordion active_section=”5″ collapsible_all=”true”][vc_tta_section title=”Registration fee” tab_id=”1497093338600-41bd8452-c2f9″][vc_column_text]General admission:

The workshop fee includes workshop materials, coffee breaks and workshop dinner. Invited speakers do not pay the workshop fee.

 [/vc_column_text][/vc_tta_section][vc_tta_section title=”Payment” tab_id=”1497093446124-f7c30913-9766″][vc_column_text]The registration fee can be paid by bank transfer only.

Conference fee should be paid to

J. J. Strossmayer University of Osijek
Department of Mathematics
Trg Ljudevita Gaja 6
31000 Osijek
Croatia

with the note: participant’s name and designation “workshop18″.
Ref.No: VAT/TIN number

Banking information

Bank name: Addiko Bank d.d
SWIFT/BIC CODE: HAABHR22
IBAN: HR37 2500 0091 4020 0004 9

Croatia

Your payment should specify that all bank charges are at your expense. Please send the confirmation of the bank transfer to the mailing address: workshop18@mathos.hr.

If you have any questions or concerns, please, send it to the mailing address: workshop18@mathos.hr.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Travel information” tab_id=”1497093338633-13cd1ea3-0cd0″][vc_column_text]Osijek can be reached by car, train, plane or bus.

By car

By train

By plane 

By bus

[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][/vc_row][/vc_section][vc_section bg_class=”BGprime” el_id=”register”][vc_row equal_height=”yes”][vc_column width=”1/2″ css=”.vc_custom_1497093954963{border-bottom-width: 0px !important;}”][vc_column_text]

You can register for the workshop by clicking the register here button

[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_row_inner content_placement=”middle”][vc_column_inner width=”1/4″][/vc_column_inner][vc_column_inner width=”1/2″][vc_empty_space height=”40px”][vc_btn title=”REGISTER HERE” shape=”square” color=”primary” size=”lg” align=”center” button_block=”true” link=”url:https%3A%2F%2Fdocs.google.com%2Fforms%2Fd%2Fe%2F1FAIpQLSfixQTdb0nb5qlM2mZ5XiQLGWzr-GWZfe34VsCH6FBcaSzQ-Q%2Fviewform%3Fusp%3Dsf_link|||” el_id=”registerHereButton”][/vc_column_inner][vc_column_inner width=”1/4″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][/vc_section][vc_section full_width=”container-wide” padding_class=”section-no-padding” bg_class=”BGlight” el_id=”participants” css=”.vc_custom_1513246668830{background-color: #dddddd !important;}”][vc_row][vc_column][proconf_section_title title=”List of participants” icon=”fa fa-address-card-o”][vc_row_inner][vc_column_inner width=”1/6″][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”2/6″][vc_column_text]

[/vc_column_text][/vc_column_inner][vc_column_inner width=”2/6″][vc_column_text]

[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/6″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][/vc_section][vc_section el_id=”organizers”][vc_row][vc_column][proconf_section_title title=”organizers” icon=”fa fa-users”][vc_row_inner][vc_column_inner width=”1/2″][vc_single_image image=”377″ img_size=”large” alignment=”right”][/vc_column_inner][vc_column_inner width=”1/2″][vc_empty_space height=”120px”][vc_column_text]

J.J. Strossmayer University of Osijek
Department of Mathematics

[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_text_separator title=”Local organizers” border_width=”2″][vc_row_inner][vc_column_inner width=”1/3″][vc_single_image image=”490″ alignment=”center”][vc_column_text css=”.vc_custom_1513351900837{margin-left: 75px !important;}”]Domagoj Matijević
email: domagoj@mathos.hr[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/3″][vc_single_image image=”465″ alignment=”center”][vc_column_text css=”.vc_custom_1513351881292{margin-left: 75px !important;}”]Matea Puvača
email: mpuvaca@mathos.hr[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/3″][vc_single_image image=”466″ alignment=”center”][vc_column_text css=”.vc_custom_1513351849723{margin-left: 75px !important;}”]Zoran Tomljanović
email: ztomljan@mathos.hr[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][/vc_section][vc_section el_id=”sponsors”][vc_row][vc_column][proconf_section_title title=”sponsors” icon=”fa fa-star”][/vc_column][/vc_row][vc_row][vc_column width=”1/3″][vc_single_image image=”582″ img_size=”150×150″ add_caption=”yes” alignment=”center”][proconf_sponsors_slider][/vc_column][vc_column width=”1/3″][vc_single_image image=”579″ alignment=”center”][/vc_column][vc_column width=”1/3″][vc_single_image image=”613″ alignment=”center”][/vc_column][/vc_row][/vc_section][vc_section full_width=”container-wide” padding_class=”section-no-padding” el_id=”contact”][vc_row][vc_column][proconf_contact_map image=”http://workshop.mathos.unios.hr/wp-content/uploads/2017/12/IMG_2828.jpg” latitude=”45.558553″ longitude=”18.684640″][proconf_contact_info title=”Classroom 1″ subtitle=”100 Seating Capacity” adress=”Department of Mathematics
Trg Ljudevita Gaja 6
HR-31000 Osijek
Croatia” phone=”+385.31.224.800″ email=”workshop18@mathos.hr”][/proconf_contact_map][/vc_column][/vc_row][/vc_section][vc_section padding_class=”section-no-padding” bg_class=”BGdark”][vc_row][vc_column][proconf_onepage_footer icons=”%5B%5D”]Department of Mathematics, Osijek, Croatia © 2017. All Rights Reserved.[/proconf_onepage_footer][/vc_column][/vc_row][/vc_section]