Space-time Galerkin POD for optimal control of Burgers equation. April 27, 2017 Absolventen Seminar Numerische Mathematik, TU Berlin

Size: px
Start display at page:

Download "Space-time Galerkin POD for optimal control of Burgers equation. April 27, 2017 Absolventen Seminar Numerische Mathematik, TU Berlin"

Transcription

1 Space-ime Galerkin POD for opimal conrol of Burgers equaion Manuel Baumann Peer Benner Jan Heiland April 27, 207 Absolvenen Seminar Numerische Mahemaik, TU Berlin

2 Ouline. Inroducion 2. Opimal Space Time Produc Bases 3. Relaion o POD 4. Space-Time Galerkin-POD for Opimal Conrol Jan Heiland Space-ime Galerkin POD 2/9

3 Inroducion ẋ = f (, ) Consider he soluion of a PDE: L 2 (I ; L 2 (Ω)) and is numerical approimaion: wih I R... he ime-inerval Ω R n... he spaial domain S Y wih S L 2 (I )... discreized ime Y L 2 (Ω)... a FE space Task: Find Ŝ S and Ŷ Y of much smaller dimension o epress. Jan Heiland Space-ime Galerkin POD 3/9

4 Space-Time Spaces Consider finie dimensional subspaces S = span{ψ,, ψ s } L 2 (I ) Y = span{ν,, ν q } L 2 (Ω) wih he mass marices M S = [ ] (ψ i, ψ j ) L 2 and M i,j=,...,s Y = [ ] (ν i, ν j ) L 2 and he produc space S Y L 2 (I ; L 2 (Ω)). PDE soluion L 2 (I ; L 2 (Ω)) S L 2 (I )... discreized ime Y L 2 (Ω)... a FE space i,j=,...,q Jan Heiland Space-ime Galerkin POD 4/9

5 Space-Time Spaces We represen a funcion = s q i j ν i ψ j S Y j= i= via is mari of coefficiens X = [ i j ] j=,...,s i=,...,q Rq,s and vice versa. Jan Heiland Space-ime Galerkin POD 5/9

6 Opimal Bases Lemma (Opimal low-rank bases in space ) Given S Y and he associaed mari of coefficiens X. The bes-approimaing subspace Ŷ in he sense ha Π S Ŷ S Y is minimal over all subspaces of Y of dimension ˆq is given as span{ˆν i } i=,...,ˆq, where ˆν ν ˆν 2. = V q Ṱ ν 2 M /2 Y., ν q ˆνˆq where Vˆq is he mari of he ˆq leading lef singular vecors of M /2 Y XM/2 S. BM&PB&JH 6: ArXiv: Jan Heiland Space-ime Galerkin POD 6/9

7 Opimal Bases The same argumens apply o he ranspose of X: Lemma (Opimal low-rank bases in ime 2 ) Given S Y and he associaed mari of coefficiens X. The bes-approimaing subspace Ŝ in he sense ha ΠŜ Y S Y is minimal over all subspaces of S of dimension ŝ is given as span{ ˆψ j } j=,...,ŝ, where ˆψ ψ ˆψ 2 ψ 2. ˆψŝ = UṰ s M /2 S., where Uŝ is he mari of he ŝ leading righ singular vecors of M /2 Y XM/2 S. ψ s 2 BM&PB&JH 6: ArXiv: Jan Heiland Space-ime Galerkin POD 7/9

8 The soluion of a spaially discreized PDE Relaion o POD : τ R q is projeced o S R q via (, ψ ) L 2... (, ψ s ) L 2 Π S Y =..... M S. ( q, ψ ) L 2... ( q, ψ s ) L 2 In he (degeneraed) case ha ψ j is a dela disribuion cenered a τ j I, he coefficien mari degeneraes o (τ )... (τ s )..... q (τ )... q (τ s ) he sandard POD snapsho mari. Jan Heiland Space-ime Galerkin POD 8/9

9 Secion 4 Space-Time Galerkin-POD for Opimal Conrol Jan Heiland Space-ime Galerkin POD 9/9

10 Targe : Sep funcion Figure : Illusraion of he sae, he adjoin, and he arge and heir approimaion via POD-reduced space-ime bases. Jan Heiland Space-ime Galerkin POD 0/9

11 Targe 2: Hear shape Figure : Illusraion of he sae, he adjoin, and he arge and heir approimaion via POD-reduced space-ime bases. Jan Heiland Space-ime Galerkin POD /9

12 Space-Time Galerkin-POD for Opimal Conrol For a arge rajecory L 2 (0, T ; L 2 (Ω)) and a penalizaion parameer α > 0, consider J (, u) := 2 2 L 2 + α 2 u 2 L 2 subjec o he generic PDE min u L 2 (0,T ;L 2 (Ω)) ẋ + N() = f + u, (0) = 0. (FWD) If he nonlineariy is smooh, hen necessary opimaliy condiions for (, u) are given hrough u = αλ, where λ solves he adjoin equaion λ λ + D N() T λ + =, λ(t ) = 0. (BWD) Jan Heiland Space-ime Galerkin POD 2/9

13 Space-Time Galerkin-POD for Opimal Conrol Algorihm:. Do sandard forward/backward solves o compue he mari of measuremens for and λ. 2. Compue opimal low-dimensional spaces Ŝ, ˆR, Ŷ, and ˆΛ for he space and ime discreizaion of he sae and he adjoin sae λ. 3. Solve he space-ime Galerkin projeced necessary opimaliy condiions (FWD)-(BWD) 3 for he reduced cosae ˆλ. 4. Define he subopimal conrol via û = α ˆλ and inflae i o he full space. 5. Apply i in he full order simulaion. 3 (FWD)-(BWD) is a wo-poin boundary value problem wih iniial and erminal condiions for which ime sepping schemes like RKM do no apply. Jan Heiland Space-ime Galerkin POD 3/9

14 Numerical Seup The PDE D Burger s equaion I = (0, ], Ω = (0, ) Viscosiy: ν = Sepfuncion as iniial value Zero Dirichle condiions The opimizaion arge : keep he iniial sae arge 2: make a hear parameer: α = 0 3 The full model Equidisan space and ime grids S = R linear ha funcions Y = Λ linear ha funcions The reduced model Ŷ = ˆΛ... of dimension ˆq = ˆp Ŝ ˆR... of dimensions ŝ = ˆr ˆq, ˆp, ŝ, ˆr... varying n... varying 4 4 dimension of ime paramerizaion for an gradien based approach Jan Heiland Space-ime Galerkin POD 4/9

15 Targe : Sep funcion ˆK ˆ 0 2 L J (ˆ, û) wallime [s] Table : Performance of he subopimal conrol versus he cumulaive dimension ˆK = ˆp + ˆq + ˆr + ŝ of he reduced bases wih ˆp = ˆq = ˆr = ŝ. (ˆq, ŝ)/(ˆp, ˆr) (6, 7) (5,0) (2,0) (0,2) (0,5) ( 7,6) 2 ˆ 0 2 L J (ˆ, û) wallime Table : Performance of he subopimal conrol versus varying disribuions of space and ime resoluions. Jan Heiland Space-ime Galerkin POD 5/9

16 Targe : Sep funcion (ˆq, ŝ)/(ˆp, ˆr) (6, 7) (5,0) (2,0) (0,2) (0,5) ( 7,6) 2 ˆ 0 2 L J (ˆ, û) wallime Table : Performance of he subopimal conrol versus varying disribuions of space and ime resoluions. (ˆq, n ) (3, 8) (5, 9) (6, 20) (9, 5) (20, 6) (8, 3) 2 ˆ 0 2 L J (ˆ, û) wallime Table : Benchmark of an gradien based approach (SQP-POD-BFGS wih α = ) Jan Heiland Space-ime Galerkin POD 6/9

17 Conclusion The space-ime Galerkin POD approach allows for consrucion of opimized Galerkin bases in space and ime in a funcional analyical framework The resuling space-ime Galerkin discreizaion approimaes PDEs by a small sysem of algebraic equaions and naurally eends o boundary value problems in ime can be used for efficien compuaions of (sub)opimal conrols Fuure work: Use he funcional analyical framework for error esimaes. Eploi he freedom of he choice of he measuremen funcions in Y, o produce, e.g., opimal measuremens or o compensae for sochasic perurbaions. Jan Heiland Space-ime Galerkin POD 7/9

18 Furher Reading and Coding M. Baumann, P. Benner, and J. Heiland. Space-Time Galerkin POD wih applicaion in opimal conrol of semi-linear parabolic parial differenial equaions. ArXiv: , Nov M. Baumann, J. Heiland, and M. Schmid. Discree inpu/oupu maps and heir relaion o POD. In P. Benner e al., ediors, Numerical Algebra, Mari Theory, Differenial-Algebraic Equaions and Conrol Theory, pages Springer, 205. J. Heiland and M. Baumann. spaceime-galerkin-pod-bfgs-ess Pyhon/Malab implemenaion space-ime POD and BFGS for opimal conrol of Burgers equaion. 206, doi:0.528/zenodo Jan Heiland Space-ime Galerkin POD 8/9

19 Thank you! Thank you for your aenion! I am always open for discussion heiland@mpi-magdeburg.mpg.de gihub.com/highlando Jan Heiland Space-ime Galerkin POD 9/9

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon 3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of

More information

An introduction to the theory of SDDP algorithm

An introduction to the theory of SDDP algorithm An inroducion o he heory of SDDP algorihm V. Leclère (ENPC) Augus 1, 2014 V. Leclère Inroducion o SDDP Augus 1, 2014 1 / 21 Inroducion Large scale sochasic problem are hard o solve. Two ways of aacking

More information

Notes on Kalman Filtering

Notes on Kalman Filtering Noes on Kalman Filering Brian Borchers and Rick Aser November 7, Inroducion Daa Assimilaion is he problem of merging model predicions wih acual measuremens of a sysem o produce an opimal esimae of he curren

More information

Random Walk on Circle Imagine a Markov process governing the random motion of a particle on a circular

Random Walk on Circle Imagine a Markov process governing the random motion of a particle on a circular Random Walk on Circle Imagine a Markov process governing he random moion of a paricle on a circular laice: 1 2 γ γ γ The paricle moves o he righ or lef wih probabiliy γ and says where i is wih probabiliy

More information

1 Subdivide the optimization horizon [t 0,t f ] into n s 1 control stages,

1 Subdivide the optimization horizon [t 0,t f ] into n s 1 control stages, Opimal Conrol Formulaion Opimal Conrol Lecures 19-2: Direc Soluion Mehods Benoî Chachua Deparmen of Chemical Engineering Spring 29 We are concerned wih numerical soluion procedures for

More information

Zürich. ETH Master Course: L Autonomous Mobile Robots Localization II

Zürich. ETH Master Course: L Autonomous Mobile Robots Localization II Roland Siegwar Margaria Chli Paul Furgale Marco Huer Marin Rufli Davide Scaramuzza ETH Maser Course: 151-0854-00L Auonomous Mobile Robos Localizaion II ACT and SEE For all do, (predicion updae / ACT),

More information

Ordinary Differential Equations

Ordinary Differential Equations Lecure 22 Ordinary Differenial Equaions Course Coordinaor: Dr. Suresh A. Karha, Associae Professor, Deparmen of Civil Engineering, IIT Guwahai. In naure, mos of he phenomena ha can be mahemaically described

More information

GENERALIZATION OF THE FORMULA OF FAA DI BRUNO FOR A COMPOSITE FUNCTION WITH A VECTOR ARGUMENT

GENERALIZATION OF THE FORMULA OF FAA DI BRUNO FOR A COMPOSITE FUNCTION WITH A VECTOR ARGUMENT Inerna J Mah & Mah Sci Vol 4, No 7 000) 48 49 S0670000970 Hindawi Publishing Corp GENERALIZATION OF THE FORMULA OF FAA DI BRUNO FOR A COMPOSITE FUNCTION WITH A VECTOR ARGUMENT RUMEN L MISHKOV Received

More information

Recursive Least-Squares Fixed-Interval Smoother Using Covariance Information based on Innovation Approach in Linear Continuous Stochastic Systems

Recursive Least-Squares Fixed-Interval Smoother Using Covariance Information based on Innovation Approach in Linear Continuous Stochastic Systems 8 Froniers in Signal Processing, Vol. 1, No. 1, July 217 hps://dx.doi.org/1.2266/fsp.217.112 Recursive Leas-Squares Fixed-Inerval Smooher Using Covariance Informaion based on Innovaion Approach in Linear

More information

Lecture 20: Riccati Equations and Least Squares Feedback Control

Lecture 20: Riccati Equations and Least Squares Feedback Control 34-5 LINEAR SYSTEMS Lecure : Riccai Equaions and Leas Squares Feedback Conrol 5.6.4 Sae Feedback via Riccai Equaions A recursive approach in generaing he marix-valued funcion W ( ) equaion for i for he

More information

2.160 System Identification, Estimation, and Learning. Lecture Notes No. 8. March 6, 2006

2.160 System Identification, Estimation, and Learning. Lecture Notes No. 8. March 6, 2006 2.160 Sysem Idenificaion, Esimaion, and Learning Lecure Noes No. 8 March 6, 2006 4.9 Eended Kalman Filer In many pracical problems, he process dynamics are nonlinear. w Process Dynamics v y u Model (Linearized)

More information

SUFFICIENT CONDITIONS FOR EXISTENCE SOLUTION OF LINEAR TWO-POINT BOUNDARY PROBLEM IN MINIMIZATION OF QUADRATIC FUNCTIONAL

SUFFICIENT CONDITIONS FOR EXISTENCE SOLUTION OF LINEAR TWO-POINT BOUNDARY PROBLEM IN MINIMIZATION OF QUADRATIC FUNCTIONAL HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, Series A, OF HE ROMANIAN ACADEMY Volume, Number 4/200, pp 287 293 SUFFICIEN CONDIIONS FOR EXISENCE SOLUION OF LINEAR WO-POIN BOUNDARY PROBLEM IN

More information

Probabilistic Robotics

Probabilistic Robotics Probabilisic Roboics Bayes Filer Implemenaions Gaussian filers Bayes Filer Reminder Predicion bel p u bel d Correcion bel η p z bel Gaussians : ~ π e p N p - Univariae / / : ~ μ μ μ e p Ν p d π Mulivariae

More information

On-line Adaptive Optimal Timing Control of Switched Systems

On-line Adaptive Optimal Timing Control of Switched Systems On-line Adapive Opimal Timing Conrol of Swiched Sysems X.C. Ding, Y. Wardi and M. Egersed Absrac In his paper we consider he problem of opimizing over he swiching imes for a muli-modal dynamic sysem when

More information

Crossing the Bridge between Similar Games

Crossing the Bridge between Similar Games Crossing he Bridge beween Similar Games Jan-David Quesel, Marin Fränzle, and Werner Damm Universiy of Oldenburg, Deparmen of Compuing Science, Germany CMACS Seminar CMU, Pisburgh, PA, USA 2nd December

More information

Augmented Reality II - Kalman Filters - Gudrun Klinker May 25, 2004

Augmented Reality II - Kalman Filters - Gudrun Klinker May 25, 2004 Augmened Realiy II Kalman Filers Gudrun Klinker May 25, 2004 Ouline Moivaion Discree Kalman Filer Modeled Process Compuing Model Parameers Algorihm Exended Kalman Filer Kalman Filer for Sensor Fusion Lieraure

More information

Georey E. Hinton. University oftoronto. Technical Report CRG-TR February 22, Abstract

Georey E. Hinton. University oftoronto.   Technical Report CRG-TR February 22, Abstract Parameer Esimaion for Linear Dynamical Sysems Zoubin Ghahramani Georey E. Hinon Deparmen of Compuer Science Universiy oftorono 6 King's College Road Torono, Canada M5S A4 Email: zoubin@cs.orono.edu Technical

More information

Sumudu Decomposition Method for Solving Fractional Delay Differential Equations

Sumudu Decomposition Method for Solving Fractional Delay Differential Equations vol. 1 (2017), Aricle ID 101268, 13 pages doi:10.11131/2017/101268 AgiAl Publishing House hp://www.agialpress.com/ Research Aricle Sumudu Decomposiion Mehod for Solving Fracional Delay Differenial Equaions

More information

Class Meeting # 10: Introduction to the Wave Equation

Class Meeting # 10: Introduction to the Wave Equation MATH 8.5 COURSE NOTES - CLASS MEETING # 0 8.5 Inroducion o PDEs, Fall 0 Professor: Jared Speck Class Meeing # 0: Inroducion o he Wave Equaion. Wha is he wave equaion? The sandard wave equaion for a funcion

More information

Hamburger Beiträge zur Angewandten Mathematik

Hamburger Beiträge zur Angewandten Mathematik Hamburger Beiräge zur Angewanden Mahemaik Asympoic Sabiliy of POD based Model Predicive Conrol for a semilinear parabolic PDE Alessandro Alla and Sefan Volkwein r. 24-8 Sepember 24 Asympoic Sabiliy of

More information

The fundamental mass balance equation is ( 1 ) where: I = inputs P = production O = outputs L = losses A = accumulation

The fundamental mass balance equation is ( 1 ) where: I = inputs P = production O = outputs L = losses A = accumulation Hea (iffusion) Equaion erivaion of iffusion Equaion The fundamenal mass balance equaion is I P O L A ( 1 ) where: I inpus P producion O oupus L losses A accumulaion Assume ha no chemical is produced or

More information

CERTAIN CLASSES OF SOLUTIONS OF LAGERSTROM EQUATIONS

CERTAIN CLASSES OF SOLUTIONS OF LAGERSTROM EQUATIONS SARAJEVO JOURNAL OF MATHEMATICS Vol.10 (22 (2014, 67 76 DOI: 10.5644/SJM.10.1.09 CERTAIN CLASSES OF SOLUTIONS OF LAGERSTROM EQUATIONS ALMA OMERSPAHIĆ AND VAHIDIN HADŽIABDIĆ Absrac. This paper presens sufficien

More information

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XI Control of Stochastic Systems - P.R. Kumar

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XI Control of Stochastic Systems - P.R. Kumar CONROL OF SOCHASIC SYSEMS P.R. Kumar Deparmen of Elecrical and Compuer Engineering, and Coordinaed Science Laboraory, Universiy of Illinois, Urbana-Champaign, USA. Keywords: Markov chains, ransiion probabiliies,

More information

An Introduction to Malliavin calculus and its applications

An Introduction to Malliavin calculus and its applications An Inroducion o Malliavin calculus and is applicaions Lecure 5: Smoohness of he densiy and Hörmander s heorem David Nualar Deparmen of Mahemaics Kansas Universiy Universiy of Wyoming Summer School 214

More information

REVERSE COMPUTATION OF FORCED CONVECTION HEAT TRANSFER USING ADJOINT FORMULATION

REVERSE COMPUTATION OF FORCED CONVECTION HEAT TRANSFER USING ADJOINT FORMULATION REVERSE COMPUTATION OF FORCED CONVECTION HEAT TRANSFER USING ADJOINT FORMULATION Kazunari Momose and Hideo Kimoo Graduae School of Engineering Science, Osaka Universiy Osaka 56-853, Japan E-mail: momose@me.es.osaka-u.ac.jp

More information

Analytical Solutions of an Economic Model by the Homotopy Analysis Method

Analytical Solutions of an Economic Model by the Homotopy Analysis Method Applied Mahemaical Sciences, Vol., 26, no. 5, 2483-249 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/.2988/ams.26.6688 Analyical Soluions of an Economic Model by he Homoopy Analysis Mehod Jorge Duare ISEL-Engineering

More information

Object tracking: Using HMMs to estimate the geographical location of fish

Object tracking: Using HMMs to estimate the geographical location of fish Objec racking: Using HMMs o esimae he geographical locaion of fish 02433 - Hidden Markov Models Marin Wæver Pedersen, Henrik Madsen Course week 13 MWP, compiled June 8, 2011 Objecive: Locae fish from agging

More information

Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation

Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation Mulifideliy precondiioning of he cross-enropy mehod for rare even simulaion and failure probabiliy esimaion Benjamin Pehersorfer, Boris Kramer, and Karen Willcox Absrac. Accuraely esimaing rare even probabiliies

More information

NUMERICAL SHOOTING METHODS FOR OPTIMAL BOUNDARY CONTROL AND EXACT BOUNDARY CONTROL OF 1-D WAVE EQUATIONS

NUMERICAL SHOOTING METHODS FOR OPTIMAL BOUNDARY CONTROL AND EXACT BOUNDARY CONTROL OF 1-D WAVE EQUATIONS INTERNATIONAL JOURNAL OF NUMERICAL ANALYSIS AND MODELING Volume, Number, Pages c 6 Insiue for Scienific Compuing and Informaion NUMERICAL SHOOTING METHODS FOR OPTIMAL BOUNDARY CONTROL AND EXACT BOUNDARY

More information

Energy Storage and Renewables in New Jersey: Complementary Technologies for Reducing Our Carbon Footprint

Energy Storage and Renewables in New Jersey: Complementary Technologies for Reducing Our Carbon Footprint Energy Sorage and Renewables in New Jersey: Complemenary Technologies for Reducing Our Carbon Fooprin ACEE E-filliaes workshop November 14, 2014 Warren B. Powell Daniel Seingar Harvey Cheng Greg Davies

More information

Stochastic Model Predictive Control for Gust Alleviation during Aircraft Carrier Landing

Stochastic Model Predictive Control for Gust Alleviation during Aircraft Carrier Landing Ouline Moivaion Sochasic formulaion Sochasic Model Predicive Conrol for Gus Alleviaion during Aircraf Carrier Landing Aircraf and gus modeling Resuls Deparmen of Mechanical and Aerospace Engineering Rugers,

More information

Applications in Industry (Extended) Kalman Filter. Week Date Lecture Title

Applications in Industry (Extended) Kalman Filter. Week Date Lecture Title hp://elec34.com Applicaions in Indusry (Eended) Kalman Filer 26 School of Informaion echnology and Elecrical Engineering a he Universiy of Queensland Lecure Schedule: Week Dae Lecure ile 29-Feb Inroducion

More information

Introduction to Mobile Robotics

Introduction to Mobile Robotics Inroducion o Mobile Roboics Bayes Filer Kalman Filer Wolfram Burgard Cyrill Sachniss Giorgio Grisei Maren Bennewiz Chrisian Plagemann Bayes Filer Reminder Predicion bel p u bel d Correcion bel η p z bel

More information

Homotopy Perturbation Method for Solving Some Initial Boundary Value Problems with Non Local Conditions

Homotopy Perturbation Method for Solving Some Initial Boundary Value Problems with Non Local Conditions Proceedings of he World Congress on Engineering and Compuer Science 23 Vol I WCECS 23, 23-25 Ocober, 23, San Francisco, USA Homoopy Perurbaion Mehod for Solving Some Iniial Boundary Value Problems wih

More information

Hybrid Control and Switched Systems. Lecture #3 What can go wrong? Trajectories of hybrid systems

Hybrid Control and Switched Systems. Lecture #3 What can go wrong? Trajectories of hybrid systems Hybrid Conrol and Swiched Sysems Lecure #3 Wha can go wrong? Trajecories of hybrid sysems João P. Hespanha Universiy of California a Sana Barbara Summary 1. Trajecories of hybrid sysems: Soluion o a hybrid

More information

Simulation of BSDEs and. Wiener Chaos Expansions

Simulation of BSDEs and. Wiener Chaos Expansions Simulaion of BSDEs and Wiener Chaos Expansions Philippe Briand Céline Labar LAMA UMR 5127, Universié de Savoie, France hp://www.lama.univ-savoie.fr/ Sochasic Analysis Seminar Oxford, June 1, 213 Inroducion

More information

Simulation of BSDEs and. Wiener Chaos Expansions

Simulation of BSDEs and. Wiener Chaos Expansions Simulaion of BSDEs and Wiener Chaos Expansions Philippe Briand Céline Labar LAMA UMR 5127, Universié de Savoie, France hp://www.lama.univ-savoie.fr/ Workshop on BSDEs Rennes, May 22-24, 213 Inroducion

More information

Multi-scale 2D acoustic full waveform inversion with high frequency impulsive source

Multi-scale 2D acoustic full waveform inversion with high frequency impulsive source Muli-scale D acousic full waveform inversion wih high frequency impulsive source Vladimir N Zubov*, Universiy of Calgary, Calgary AB vzubov@ucalgaryca and Michael P Lamoureux, Universiy of Calgary, Calgary

More information

SPLICING OF TIME OPTIMAL CONTROLS

SPLICING OF TIME OPTIMAL CONTROLS Dynamic Sysems and Applicaions 21 (212) 169-186 SPLICING OF TIME OPTIMAL CONTROLS H. O. FATTORINI Deparmen of Mahemaics, Universiy of California Los Angeles, California 995-1555, USA ABSTRACT. Two conrols

More information

Differential Equations

Differential Equations Mah 21 (Fall 29) Differenial Equaions Soluion #3 1. Find he paricular soluion of he following differenial equaion by variaion of parameer (a) y + y = csc (b) 2 y + y y = ln, > Soluion: (a) The corresponding

More information

Haar Wavelet Operational Matrix Method for Solving Fractional Partial Differential Equations

Haar Wavelet Operational Matrix Method for Solving Fractional Partial Differential Equations Copyrigh 22 Tech Science Press CMES, vol.88, no.3, pp.229-243, 22 Haar Wavele Operaional Mari Mehod for Solving Fracional Parial Differenial Equaions Mingu Yi and Yiming Chen Absrac: In his paper, Haar

More information

Analytic Model and Bilateral Approximation for Clocked Comparator

Analytic Model and Bilateral Approximation for Clocked Comparator Analyic Model and Bilaeral Approximaion for Clocked Comparaor M. Greians, E. Hermanis, G.Supols Insiue of, Riga, Lavia, e-mail: gais.supols@edi.lv Research is suppored by: 1) ESF projec Nr.1DP/1.1.1.2.0/09/APIA/VIAA/020,

More information

Optimal Investment under Dynamic Risk Constraints and Partial Information

Optimal Investment under Dynamic Risk Constraints and Partial Information Opimal Invesmen under Dynamic Risk Consrains and Parial Informaion Wolfgang Puschögl Johann Radon Insiue for Compuaional and Applied Mahemaics (RICAM) Ausrian Academy of Sciences www.ricam.oeaw.ac.a 2

More information

ψ(t) = V x (0)V x (t)

ψ(t) = V x (0)V x (t) .93 Home Work Se No. (Professor Sow-Hsin Chen Spring Term 5. Due March 7, 5. This problem concerns calculaions of analyical expressions for he self-inermediae scaering funcion (ISF of he es paricle in

More information

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method Journal of Applied Mahemaics & Bioinformaics, vol., no., 01, 1-14 ISSN: 179-660 (prin), 179-699 (online) Scienpress Ld, 01 Improved Approimae Soluions for Nonlinear Evoluions Equaions in Mahemaical Physics

More information

STATE-SPACE MODELLING. A mass balance across the tank gives:

STATE-SPACE MODELLING. A mass balance across the tank gives: B. Lennox and N.F. Thornhill, 9, Sae Space Modelling, IChemE Process Managemen and Conrol Subjec Group Newsleer STE-SPACE MODELLING Inroducion: Over he pas decade or so here has been an ever increasing

More information

A variational radial basis function approximation for diffusion processes.

A variational radial basis function approximation for diffusion processes. A variaional radial basis funcion approximaion for diffusion processes. Michail D. Vreas, Dan Cornford and Yuan Shen {vreasm, d.cornford, y.shen}@ason.ac.uk Ason Universiy, Birmingham, UK hp://www.ncrg.ason.ac.uk

More information

Comparison between the Discrete and Continuous Time Models

Comparison between the Discrete and Continuous Time Models Comparison beween e Discree and Coninuous Time Models D. Sulsky June 21, 2012 1 Discree o Coninuous Recall e discree ime model Î = AIS Ŝ = S Î. Tese equaions ell us ow e populaion canges from one day o

More information

An recursive analytical technique to estimate time dependent physical parameters in the presence of noise processes

An recursive analytical technique to estimate time dependent physical parameters in the presence of noise processes WHAT IS A KALMAN FILTER An recursive analyical echnique o esimae ime dependen physical parameers in he presence of noise processes Example of a ime and frequency applicaion: Offse beween wo clocks PREDICTORS,

More information

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand Excel-Based Soluion Mehod For The Opimal Policy Of The Hadley And Whiin s Exac Model Wih Arma Demand Kal Nami School of Business and Economics Winson Salem Sae Universiy Winson Salem, NC 27110 Phone: (336)750-2338

More information

Numerical Dispersion

Numerical Dispersion eview of Linear Numerical Sabiliy Numerical Dispersion n he previous lecure, we considered he linear numerical sabiliy of boh advecion and diffusion erms when approimaed wih several spaial and emporal

More information

From Particles to Rigid Bodies

From Particles to Rigid Bodies Rigid Body Dynamics From Paricles o Rigid Bodies Paricles No roaions Linear velociy v only Rigid bodies Body roaions Linear velociy v Angular velociy ω Rigid Bodies Rigid bodies have boh a posiion and

More information

PENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD

PENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD PENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD HAN XIAO 1. Penalized Leas Squares Lasso solves he following opimizaion problem, ˆβ lasso = arg max β R p+1 1 N y i β 0 N x ij β j β j (1.1) for some 0.

More information

L07. KALMAN FILTERING FOR NON-LINEAR SYSTEMS. NA568 Mobile Robotics: Methods & Algorithms

L07. KALMAN FILTERING FOR NON-LINEAR SYSTEMS. NA568 Mobile Robotics: Methods & Algorithms L07. KALMAN FILTERING FOR NON-LINEAR SYSTEMS NA568 Mobile Roboics: Mehods & Algorihms Today s Topic Quick review on (Linear) Kalman Filer Kalman Filering for Non-Linear Sysems Exended Kalman Filer (EKF)

More information

Sequential Importance Resampling (SIR) Particle Filter

Sequential Importance Resampling (SIR) Particle Filter Paricle Filers++ Pieer Abbeel UC Berkeley EECS Many slides adaped from Thrun, Burgard and Fox, Probabilisic Roboics 1. Algorihm paricle_filer( S -1, u, z ): 2. Sequenial Imporance Resampling (SIR) Paricle

More information

THE EFFECT OF SUCTION AND INJECTION ON UNSTEADY COUETTE FLOW WITH VARIABLE PROPERTIES

THE EFFECT OF SUCTION AND INJECTION ON UNSTEADY COUETTE FLOW WITH VARIABLE PROPERTIES Kragujevac J. Sci. 3 () 7-4. UDC 53.5:536. 4 THE EFFECT OF SUCTION AND INJECTION ON UNSTEADY COUETTE FLOW WITH VARIABLE PROPERTIES Hazem A. Aia Dep. of Mahemaics, College of Science,King Saud Universiy

More information

Solving a System of Nonlinear Functional Equations Using Revised New Iterative Method

Solving a System of Nonlinear Functional Equations Using Revised New Iterative Method Solving a Sysem of Nonlinear Funcional Equaions Using Revised New Ieraive Mehod Sachin Bhalekar and Varsha Dafardar-Gejji Absrac In he presen paper, we presen a modificaion of he New Ieraive Mehod (NIM

More information

Multi-component Levi Hierarchy and Its Multi-component Integrable Coupling System

Multi-component Levi Hierarchy and Its Multi-component Integrable Coupling System Commun. Theor. Phys. (Beijing, China) 44 (2005) pp. 990 996 c Inernaional Academic Publishers Vol. 44, No. 6, December 5, 2005 uli-componen Levi Hierarchy and Is uli-componen Inegrable Coupling Sysem XIA

More information

Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2

Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2 Financial Economerics Kalman Filer: some applicaions o Finance Universiy of Evry - Maser 2 Eric Bouyé January 27, 2009 Conens 1 Sae-space models 2 2 The Scalar Kalman Filer 2 21 Presenaion 2 22 Summary

More information

Primal-Dual Splitting: Recent Improvements and Variants

Primal-Dual Splitting: Recent Improvements and Variants Primal-Dual Spliing: Recen Improvemens and Varians 1 Thomas Pock and 2 Anonin Chambolle 1 Insiue for Compuer Graphics and Vision, TU Graz, Ausria 2 CMAP & CNRS École Polyechnique, France The proximal poin

More information

Optimizing heat exchangers

Optimizing heat exchangers Opimizing hea echangers Jean-Luc Thiffeaul Deparmen of Mahemaics, Universiy of Wisconsin Madison, 48 Lincoln Dr., Madison, WI 5376, USA wih: Florence Marcoe, Charles R. Doering, William R. Young (Daed:

More information

Math 10B: Mock Mid II. April 13, 2016

Math 10B: Mock Mid II. April 13, 2016 Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.

More information

On the Separation Theorem of Stochastic Systems in the Case Of Continuous Observation Channels with Memory

On the Separation Theorem of Stochastic Systems in the Case Of Continuous Observation Channels with Memory Journal of Physics: Conference eries PAPER OPEN ACCE On he eparaion heorem of ochasic ysems in he Case Of Coninuous Observaion Channels wih Memory o cie his aricle: V Rozhova e al 15 J. Phys.: Conf. er.

More information

THE FOURIER-YANG INTEGRAL TRANSFORM FOR SOLVING THE 1-D HEAT DIFFUSION EQUATION. Jian-Guo ZHANG a,b *

THE FOURIER-YANG INTEGRAL TRANSFORM FOR SOLVING THE 1-D HEAT DIFFUSION EQUATION. Jian-Guo ZHANG a,b * Zhang, J.-G., e al.: The Fourier-Yang Inegral Transform for Solving he -D... THERMAL SCIENCE: Year 07, Vol., Suppl., pp. S63-S69 S63 THE FOURIER-YANG INTEGRAL TRANSFORM FOR SOLVING THE -D HEAT DIFFUSION

More information

Application of a Stochastic-Fuzzy Approach to Modeling Optimal Discrete Time Dynamical Systems by Using Large Scale Data Processing

Application of a Stochastic-Fuzzy Approach to Modeling Optimal Discrete Time Dynamical Systems by Using Large Scale Data Processing Applicaion of a Sochasic-Fuzzy Approach o Modeling Opimal Discree Time Dynamical Sysems by Using Large Scale Daa Processing AA WALASZE-BABISZEWSA Deparmen of Compuer Engineering Opole Universiy of Technology

More information

Continuous Time. Time-Domain System Analysis. Impulse Response. Impulse Response. Impulse Response. Impulse Response. ( t) + b 0.

Continuous Time. Time-Domain System Analysis. Impulse Response. Impulse Response. Impulse Response. Impulse Response. ( t) + b 0. Time-Domain Sysem Analysis Coninuous Time. J. Robers - All Righs Reserved. Edied by Dr. Rober Akl 1. J. Robers - All Righs Reserved. Edied by Dr. Rober Akl 2 Le a sysem be described by a 2 y ( ) + a 1

More information

Stability and Bifurcation in a Neural Network Model with Two Delays

Stability and Bifurcation in a Neural Network Model with Two Delays Inernaional Mahemaical Forum, Vol. 6, 11, no. 35, 175-1731 Sabiliy and Bifurcaion in a Neural Nework Model wih Two Delays GuangPing Hu and XiaoLing Li School of Mahemaics and Physics, Nanjing Universiy

More information

0.1 MAXIMUM LIKELIHOOD ESTIMATION EXPLAINED

0.1 MAXIMUM LIKELIHOOD ESTIMATION EXPLAINED 0.1 MAXIMUM LIKELIHOOD ESTIMATIO EXPLAIED Maximum likelihood esimaion is a bes-fi saisical mehod for he esimaion of he values of he parameers of a sysem, based on a se of observaions of a random variable

More information

dt = C exp (3 ln t 4 ). t 4 W = C exp ( ln(4 t) 3) = C(4 t) 3.

dt = C exp (3 ln t 4 ). t 4 W = C exp ( ln(4 t) 3) = C(4 t) 3. Mah Rahman Exam Review Soluions () Consider he IVP: ( 4)y 3y + 4y = ; y(3) = 0, y (3) =. (a) Please deermine he longes inerval for which he IVP is guaraneed o have a unique soluion. Soluion: The disconinuiies

More information

Model Reduction for Dynamical Systems Lecture 6

Model Reduction for Dynamical Systems Lecture 6 Oo-von-Guericke Universiä Magdeburg Faculy of Mahemaics Summer erm 07 Model Reducion for Dynamical Sysems ecure 6 v eer enner and ihong Feng Max lanck Insiue for Dynamics of Complex echnical Sysems Compuaional

More information

Energy Storage Benchmark Problems

Energy Storage Benchmark Problems Energy Sorage Benchmark Problems Daniel F. Salas 1,3, Warren B. Powell 2,3 1 Deparmen of Chemical & Biological Engineering 2 Deparmen of Operaions Research & Financial Engineering 3 Princeon Laboraory

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION DOI: 0.038/NCLIMATE893 Temporal resoluion and DICE * Supplemenal Informaion Alex L. Maren and Sephen C. Newbold Naional Cener for Environmenal Economics, US Environmenal Proecion

More information

A NEW TECHNOLOGY FOR SOLVING DIFFUSION AND HEAT EQUATIONS

A NEW TECHNOLOGY FOR SOLVING DIFFUSION AND HEAT EQUATIONS THERMAL SCIENCE: Year 7, Vol., No. A, pp. 33-4 33 A NEW TECHNOLOGY FOR SOLVING DIFFUSION AND HEAT EQUATIONS by Xiao-Jun YANG a and Feng GAO a,b * a School of Mechanics and Civil Engineering, China Universiy

More information

Single-loop System Reliability-based Topology Optimization Accounting for Statistical Dependence between Limit-states

Single-loop System Reliability-based Topology Optimization Accounting for Statistical Dependence between Limit-states 11 h US Naional Congress on Compuaional Mechanics Minneapolis, Minnesoa Single-loop Sysem Reliabiliy-based Topology Opimizaion Accouning for Saisical Dependence beween imi-saes Tam Nguyen, Norheasern Universiy

More information

Lecture 10: Wave equation, solution by spherical means

Lecture 10: Wave equation, solution by spherical means Lecure : Wave equaion, soluion by spherical means Physical modeling eample: Elasodynamics u (; ) displacemen vecor in elasic body occupying a domain U R n, U, The posiion of he maerial poin siing a U in

More information

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB Elecronic Companion EC.1. Proofs of Technical Lemmas and Theorems LEMMA 1. Le C(RB) be he oal cos incurred by he RB policy. Then we have, T L E[C(RB)] 3 E[Z RB ]. (EC.1) Proof of Lemma 1. Using he marginal

More information

A combined space discrete algorithm with a Taylor series by time for CFD

A combined space discrete algorithm with a Taylor series by time for CFD A combined space discree algorihm wih a Taylor series by ime for CFD I.V. Kazachkov Hea and Power Faculy Naional Technical Universiy of Ukraine KPI Kyiv Polyekhnichna 7 B.5 56 Ukraine SUMMARY The firs

More information

Time series model fitting via Kalman smoothing and EM estimation in TimeModels.jl

Time series model fitting via Kalman smoothing and EM estimation in TimeModels.jl Time series model fiing via Kalman smoohing and EM esimaion in TimeModels.jl Gord Sephen Las updaed: January 206 Conens Inroducion 2. Moivaion and Acknowledgemens....................... 2.2 Noaion......................................

More information

arxiv: v1 [math.na] 26 Sep 2017

arxiv: v1 [math.na] 26 Sep 2017 Ineracing paricle filers for simulaneous sae and parameer esimaion Angwenyi David, Insiu für ahemaik Universiä Posdam Email: kipkoej@gmail.com Jana de Wiljes, Insiu für ahemaik Universiä Posdam Email:

More information

Excerpted Preprint Section

Excerpted Preprint Section X W V 1 Techniques in Compuaional Sochasic Dynamic rogramming Floyd. Hanson Universiy of Illinois a Chicago Chicago, Illinois 66-45 A. DIFFERENTIAL DYNAIC ROGRAING Excerped reprin Secion Differenial dynamic

More information

Structural Break Detection for a Class of Nonlinear Time Series Models

Structural Break Detection for a Class of Nonlinear Time Series Models Srucural Break Deecion for a Class of Nonlinear Time Series Models Richard A. Davis Thomas Lee Gabriel Rodriguez-Yam Colorado Sae Universiy (hp://www.sa.colosae.edu/~rdavis/lecures) This research suppored

More information

Structural Break Detection in Time Series Models

Structural Break Detection in Time Series Models Srucural Break Deecion in Time Series Models Richard A. Davis Thomas Lee Gabriel Rodriguez-Yam Colorado Sae Universiy (hp://www.sa.colosae.edu/~rdavis/lecures) This research suppored in par by an IBM faculy

More information

Numerical Methods for Open-Loop and Closed-Loop Optimization of Linear Control Systems

Numerical Methods for Open-Loop and Closed-Loop Optimization of Linear Control Systems Compuaional Mahemaics and Mahemaical Physics, Vol. 4, No. 6, 2, pp. 799 819. Translaed from Zhurnal Vychisliel noi Maemaiki i Maemaicheskoi Fiziki, Vol. 4, No. 6, 2, pp. 838 859. Original Russian Tex Copyrigh

More information

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Simulaion-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Week Descripion Reading Maerial 2 Compuer Simulaion of Dynamic Models Finie Difference, coninuous saes, discree ime Simple Mehods Euler Trapezoid

More information

Math-Net.Ru All Russian mathematical portal

Math-Net.Ru All Russian mathematical portal Mah-Ne.Ru All Russian mahemaical poral Aleksei S. Rodin, On he srucure of singular se of a piecewise smooh minimax soluion of Hamilon-Jacobi-Bellman equaion, Ural Mah. J., 2016, Volume 2, Issue 1, 58 68

More information

Kalman filtering for maximum likelihood estimation given corrupted observations.

Kalman filtering for maximum likelihood estimation given corrupted observations. alman filering maimum likelihood esimaion given corruped observaions... Holmes Naional Marine isheries Service Inroducion he alman filer is used o eend likelihood esimaion o cases wih hidden saes such

More information

RL Lecture 7: Eligibility Traces. R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1

RL Lecture 7: Eligibility Traces. R. S. Sutton and A. G. Barto: Reinforcement Learning: An Introduction 1 RL Lecure 7: Eligibiliy Traces R. S. Suon and A. G. Baro: Reinforcemen Learning: An Inroducion 1 N-sep TD Predicion Idea: Look farher ino he fuure when you do TD backup (1, 2, 3,, n seps) R. S. Suon and

More information

Open loop vs Closed Loop. Example: Open Loop. Example: Feedforward Control. Advanced Control I

Open loop vs Closed Loop. Example: Open Loop. Example: Feedforward Control. Advanced Control I Open loop vs Closed Loop Advanced I Moor Command Movemen Overview Open Loop vs Closed Loop Some examples Useful Open Loop lers Dynamical sysems CPG (biologically inspired ), Force Fields Feedback conrol

More information

arxiv: v1 [math.oc] 27 Jul 2009

arxiv: v1 [math.oc] 27 Jul 2009 PARTICLE METHODS FOR STOCHASTIC OPTIMAL CONTROL PROBLEMS PIERRE CARPENTIER GUY COHEN AND ANES DALLAGI arxiv:0907.4663v1 [mah.oc] 27 Jul 2009 Absrac. When dealing wih numerical soluion of sochasic opimal

More information

Fractional Method of Characteristics for Fractional Partial Differential Equations

Fractional Method of Characteristics for Fractional Partial Differential Equations Fracional Mehod of Characerisics for Fracional Parial Differenial Equaions Guo-cheng Wu* Modern Teile Insiue, Donghua Universiy, 188 Yan-an ilu Road, Shanghai 51, PR China Absrac The mehod of characerisics

More information

EMS SCM joint meeting. On stochastic partial differential equations of parabolic type

EMS SCM joint meeting. On stochastic partial differential equations of parabolic type EMS SCM join meeing Barcelona, May 28-30, 2015 On sochasic parial differenial equaions of parabolic ype Isván Gyöngy School of Mahemaics and Maxwell Insiue Edinburgh Universiy 1 I. Filering problem II.

More information

Pade and Laguerre Approximations Applied. to the Active Queue Management Model. of Internet Protocol

Pade and Laguerre Approximations Applied. to the Active Queue Management Model. of Internet Protocol Applied Mahemaical Sciences, Vol. 7, 013, no. 16, 663-673 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/10.1988/ams.013.39499 Pade and Laguerre Approximaions Applied o he Acive Queue Managemen Model of Inerne

More information

2. Nonlinear Conservation Law Equations

2. Nonlinear Conservation Law Equations . Nonlinear Conservaion Law Equaions One of he clear lessons learned over recen years in sudying nonlinear parial differenial equaions is ha i is generally no wise o ry o aack a general class of nonlinear

More information

Design Tolerance Optimization using LS-OPT

Design Tolerance Optimization using LS-OPT Design Tolerance Opimizaion using LS-OPT Anirban Basudhar, Nielen Sander, Imiaz Gandikoa, Åke Svedin 2, Kaharina Wiowski 3 Livermore Sofware Technology Corporaion, Livermore, CA, USA 2 DYNAmore Nordic,

More information

LOCAL A POSTERIORI ERROR ESTIMATES FOR TIME-DEPENDENT HAMILTON-JACOBI EQUATIONS

LOCAL A POSTERIORI ERROR ESTIMATES FOR TIME-DEPENDENT HAMILTON-JACOBI EQUATIONS MATHEMATICS OF COMPUTATION Volume 82, Number 28, January 23, Pages 87 22 S 25-578(22)26-X Aricle elecronically published on June 5, 22 LOCAL A POSTERIORI ERROR ESTIMATES FOR TIME-DEPENDENT HAMILTON-JACOBI

More information

Chapter 3 Boundary Value Problem

Chapter 3 Boundary Value Problem Chaper 3 Boundary Value Problem A boundary value problem (BVP) is a problem, ypically an ODE or a PDE, which has values assigned on he physical boundary of he domain in which he problem is specified. Le

More information

APPLICATION OF CHEBYSHEV APPROXIMATION IN THE PROCESS OF VARIATIONAL ITERATION METHOD FOR SOLVING DIFFERENTIAL- ALGEBRAIC EQUATIONS

APPLICATION OF CHEBYSHEV APPROXIMATION IN THE PROCESS OF VARIATIONAL ITERATION METHOD FOR SOLVING DIFFERENTIAL- ALGEBRAIC EQUATIONS Mahemaical and Compuaional Applicaions, Vol., No. 4, pp. 99-978,. Associaion for Scienific Research APPLICATION OF CHEBYSHEV APPROXIMATION IN THE PROCESS OF VARIATIONAL ITERATION METHOD FOR SOLVING DIFFERENTIAL-

More information

t dt t SCLP Bellman (1953) CLP (Dantzig, Tyndall, Grinold, Perold, Anstreicher 60's-80's) Anderson (1978) SCLP

t dt t SCLP Bellman (1953) CLP (Dantzig, Tyndall, Grinold, Perold, Anstreicher 60's-80's) Anderson (1978) SCLP Coninuous Linear Programming. Separaed Coninuous Linear Programming Bellman (1953) max c () u() d H () u () + Gsusds (,) () a () u (), < < CLP (Danzig, yndall, Grinold, Perold, Ansreicher 6's-8's) Anderson

More information

MATH 5720: Gradient Methods Hung Phan, UMass Lowell October 4, 2018

MATH 5720: Gradient Methods Hung Phan, UMass Lowell October 4, 2018 MATH 5720: Gradien Mehods Hung Phan, UMass Lowell Ocober 4, 208 Descen Direcion Mehods Consider he problem min { f(x) x R n}. The general descen direcions mehod is x k+ = x k + k d k where x k is he curren

More information

Structural Break Detection in Time Series Models

Structural Break Detection in Time Series Models Srucural Break Deecion in Time Series Models Richard A. Davis Thomas Lee Gabriel Rodriguez-Yam Colorado Sae Universiy (hp://www.sa.colosae.edu/~rdavis/lecures) This research suppored in par by an IBM faculy

More information