COSC 6374 Parallel Computation

Size: px
Start display at page:

Download "COSC 6374 Parallel Computation"

Transcription

1 COSC 67 Parallel Comptaton Partal Derental Eqatons Edgar Gabrel Fall 0 Nmercal derentaton orward derence ormla From te denton o dervatves one can derve an appromaton or te st dervatve Te same ormla can be obtaned rom te Taylor seres, e.g. lm 0 y n n n

2 Center Derence Formla A better ormla s derved lookng at te ollowng two terms Sbtractng eqaton : rom : leads to s qadratc n te error term!! [...] ] [ y n n n : : Center Derence Formla or nd Dervatves Etend : and : by an addtonal term Addng bot eqatons leads to!!!! [...] ] [

3 Nmercal derentaton - smmary Forward derence ormla: Center derence ormla or te st dervatve: [ ] Center derence ormla or te nd dervatve: [ ] Derental eqatons - termnology Derental eqatons: eqatons contanng te dervatve o a ncton as a varable An ordnary derental eqaton ODE only contans nctons o one ndependent varable A partal derental eqaton PDE contans nctons o mltple ndependent varables and ter partal dervatves Te order o a derental eqaton s tat o te gest dervatve tat t contans Te goal s to nd a ncton yt wose dervatves lll te gven derental eqatons, e.g. n n y t t, y, y, y,..., y

4 Fnte Derences Approac or Solvng Derental Eqatons I te analytc solton o te DE can not be determned, calclate an appromate solton n dscrete locatons Replace te dervatves n te DE by an accordng appromaton ormla y t [ y t y t ] y t [ y t y t y t ] Eample I Solve te ollowng two pont bondary vale problem sng te nte derence metod d y dy d d y 0 y Lets assme te ponts o nterest are eqally spaced b a a 0 n n e.g. or =0., te mes ponts are 0 0, 0., 0., 0.6, 0.8, 5.0 De to te bondary vales: y - y are nknown y 0 y 0 y 5 y 5

5 Eample II Dscrete verson o te ODE sng central derences: y 0 0 y y y y y 0 0 y y y y y y y 5 y y y 0y y 0 : Eample III 0y0 50y 0y y 0y 00. : 50y 0y : 0y 50y 0y : 0y 50y 0y 6 : 0y 50y 68 or y y 0 y 50y 6 68 A y b 5

6 Solvng Ay=b sng an teratve solver e.g. te B-CGSTAB algortm Gven A,b and an ntal gess y 0 r0 b Ay 0 Gven rˆ sc tat rˆ T 0 r v p or =,, rˆ T 0 r p r v p v Ap T rˆ 0 v s r v t As T t s T t t y y p s r s t Matr-vector mltplcaton Scalar prodct Scalar prodct n parallel Scalar prodct: s N 0 a[ ]* b[ ] Parallel algortm s N / 0 N / a[ ]* b[ ] N N / a[ ]* b[ ] N / alocal[ ]* blocal[ ] alocal[ ]* blocal[ ] 0 0 rank0 Process wt rank=0 a 0... N b 0... N a N... N b N... N rank reqres commncaton between te processes Process wt rank= 6

7 Matr-vector prodct n parallel rs rs 0 rs 50 rs Process 0 Process rs rs 0 50 rs rs Process 0 needs Process needs Matr vector prodct n parallel II Introdcton o gost cells Process zero Process one Lookng at te sorce code, e.g p r v Ap p v snce te vector sed n te matr-vector mltplcaton canges every teraton, yo ave to pdate te gost cells beore every matr-vector mltply operaton 7

8 8 Matr vector prodct n parallel III so te parallel algortm or te same area s: v p r p Ap v Update te gost-cells o p, e.g - Process 0 sends p to Process - Process sends p to Process 0 D Eample - Laplace eqaton I -D Laplace eqaton Central dscretzaton leads to 0,, y y y 0,,,,,, j j j j j j,j -,j +,j,j+,j-

9 -D Eample: Laplace eqaton II Parallel doman decomposton Data ecange at process bondares reqred Halo cells / Gost cells Copy o te last row/colmn o data rom te negbor process Eample -D Laplace eqaton IV Process mappng and determnng negbor processes np : no o procs n drecton y npy : no o procs n y drecton n rank n n let rgt n p down rank rank np rank np ,,,, ,,,, 0 0,0,0,0,0 At bondares: set te rank o te accordng negbor to MPI_PROC_NULL a message sent to MPI_PROC_NULL wll be gnored by te MPI lbrary Easer: se cartesan topology nctons 9

10 Data storage wt gost-cells,j s stored n a matr!!assmng C!! nlocal : no o local ponts n drecton nylocal : no o local ponts n y drecton Dmenson o on an nner process = not beng at a bondary: n local, n ylocal wt : n local,: n contanng te local data ylocal gostcells need to be taken nto accont or loops, e.g. or =; < nlocal+; ++ { or j=; j<nylocal+; j++ { [][j] = } } y rom n p to n p to n let rom n let n nylocal + n nylocal to n rgt rom n rgt, n ylocal to be sent to n let 0 n local, n ylocal to be sent to n rgt n local, to be sent to n down n local, n ylocal to be sent to n p 0, n ylocal to be receved rom n let n local +, n ylocal to be receve rom n rgt n local, 0 to be receved rom n down n local, n ylocal + to be receved rom n p 0 to n down rom n down n local + n local 0

11 Commncaton n y-drecton MPI_Reqest req[8]; MPI_Irecv&[][nylocal+], nlocal, MPI_DOUBLE, np, tag, comm, &req[0]; MPI_Irecv&[][0], nlocal, MPI_DOUBLE, ndown, tag, comm, &req[]; MPI_Isend&[][nylocal], nlocal, MPI_DOUBLE, np, tag, comm, &req[]; MPI_Isend&[][], nlocal, MPI_DOUBLE, ndown, tag, comm, &req[]; // Watall mgt be postponed ntl commncaton // n -drecton as also been posted MPI_Watall, req, MPI_STATUSES_IGNORE; Commncaton n -drecton I Problem: te data wc we ave to send s not contgos n te memory Logcal vew o te matr Layot n memory o te same matr n C

12 Commncaton n -drecton II How to mplement te alo-cell ecange n - drecton? Send/Recv every element n a separate message + tecncally correct - very slow - derved datatypes - copy te data nto a separate vector/array and send ts array + works a more general nterace s provded by MPI to pack data nto a contgos ber beore sendng Commncaton n -drecton III doble *sblet, *sbrg, *rblet, *rbrgt; /* allocate te temporary bers */ sblet = malloc nylocal * szeodoble; sbrgt = malloc nylocal * szeodoble; rblet = malloc nylocal * szeodoble; rbrgt = malloc nylocal * szeodoble; sblet == NULL sbrgt == NULL rblet == NULL rbrgt == NULL { prnt Cold not allocate memory!\n ; MPI_Abort MPI_COMM_WORLD, ; } /* Pack te data beore sendng */ or j=0, =; <nylocal+; ++, j++ { sbrgt[j] = [nlocal][]; sblet[j] = [][]; }

13 Commncaton n -drecton IV /* Eecte now te real commncaton */ MPI_Irecvrblet, nylocal, MPI_DOUBLE, nlet, tag, comm, &req[0]; MPI_Irecvrbrgt, nylocal, MPI_DOUBLE, nrgt,tag, comm, &req[]; MPI_Isendsblet, nylocal, MPI_DOUBLE, nlet, tag, comm, &req[]; MPI_Isendsbrgt, nylocal, MPI_DOUBLE, nrgt, tag, comm, &req[]; MPI_Watall, req, MPI_STATUSES_IGNORE; /* Unpack te receved data */ or j=0, =; <nylocal+; ++, j++ { [nlocal+][] = rbrgt[j]; [0][] = rblet[j]; } ree rblet ; ree rbrgt ; ree sblet ; ree sbrgt ; Derved data types vs. pack/npack Advantages o derved datatypes: avods temporary bers smpler code potentally aster Advantages o pack/npack mgt lead to perormance advantages te same packed ber as to be sent to mltple targets many sers nd pack/npack nttve smlar to smply copyng te data tems nto a temporary ber

COSC 4397 Parallel Computation

COSC 4397 Parallel Computation COSC 4397 Solvng the Laplace Equaton wth MPI Sprng Numercal dfferentaton forward dfference formula y From the defnton of dervatves f( x+ f( f ( = lm h h one can derve an approxmaton for the st dervatve

More information

ORDINARY DIFFERENTIAL EQUATIONS EULER S METHOD

ORDINARY DIFFERENTIAL EQUATIONS EULER S METHOD Numercal Analss or Engneers German Jordanan Unverst ORDINARY DIFFERENTIAL EQUATIONS We wll eplore several metods o solvng rst order ordnar derental equatons (ODEs and we wll sow ow tese metods can be appled

More information

Lecture 26 Finite Differences and Boundary Value Problems

Lecture 26 Finite Differences and Boundary Value Problems 4//3 Leture 6 Fnte erenes and Boundar Value Problems Numeral derentaton A nte derene s an appromaton o a dervatve - eample erved rom Talor seres 3 O! Negletng all terms ger tan rst order O O Tat s te orward

More information

Shuai Dong. Isaac Newton. Gottfried Leibniz

Shuai Dong. Isaac Newton. Gottfried Leibniz Computatonal pyscs Sua Dong Isaac Newton Gottred Lebnz Numercal calculus poston dervatve ntegral v velocty dervatve ntegral a acceleraton Numercal calculus Numercal derentaton Numercal ntegraton Roots

More information

AE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept.

AE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept. AE/ME 339 Comptatonal Fld Dynamcs (CFD) Comptatonal Fld Dynamcs (AE/ME 339) Implct form of dfference eqaton In the prevos explct method, the solton at tme level n,,n, depended only on the known vales of,

More information

Numerical Differentiation

Numerical Differentiation Part 5 Capter 19 Numercal Derentaton PowerPonts organzed by Dr. Mcael R. Gustason II, Duke Unversty Revsed by Pro. Jang, CAU All mages copyrgt Te McGraw-Hll Companes, Inc. Permsson requred or reproducton

More information

Chapter 3 Differentiation and Integration

Chapter 3 Differentiation and Integration MEE07 Computer Modelng Technques n Engneerng Chapter Derentaton and Integraton Reerence: An Introducton to Numercal Computatons, nd edton, S. yakowtz and F. zdarovsky, Mawell/Macmllan, 990. Derentaton

More information

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems )

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems ) Lecture Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods Numercal Methods Bracketng Methods Open Methods Convergence Notatons Root Fndng

More information

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method BAR & TRUSS FINITE ELEMENT Drect Stness Method FINITE ELEMENT ANALYSIS AND APPLICATIONS INTRODUCTION TO FINITE ELEMENT METHOD What s the nte element method (FEM)? A technqe or obtanng approxmate soltons

More information

Finite Difference Method

Finite Difference Method 7/0/07 Instructor r. Ramond Rump (9) 747 698 rcrump@utep.edu EE 337 Computatonal Electromagnetcs (CEM) Lecture #0 Fnte erence Method Lecture 0 These notes ma contan coprghted materal obtaned under ar use

More information

Parameter estimation class 5

Parameter estimation class 5 Parameter estmaton class 5 Multple Ve Geometr Comp 9-89 Marc Pollefes Content Background: Projectve geometr (D, 3D), Parameter estmaton, Algortm evaluaton. Sngle Ve: Camera model, Calbraton, Sngle Ve Geometr.

More information

SE Story Shear Frame. Final Project. 2 Story Bending Beam. m 2. u 2. m 1. u 1. m 3. u 3 L 3. Given: L 1 L 2. EI ω 1 ω 2 Solve for m 2.

SE Story Shear Frame. Final Project. 2 Story Bending Beam. m 2. u 2. m 1. u 1. m 3. u 3 L 3. Given: L 1 L 2. EI ω 1 ω 2 Solve for m 2. SE 8 Fnal Project Story Sear Frame Gven: EI ω ω Solve for Story Bendng Beam Gven: EI ω ω 3 Story Sear Frame Gven: L 3 EI ω ω ω 3 3 m 3 L 3 Solve for Solve for m 3 3 4 3 Story Bendng Beam Part : Determnng

More information

CHAPTER 4d. ROOTS OF EQUATIONS

CHAPTER 4d. ROOTS OF EQUATIONS CHAPTER 4d. ROOTS OF EQUATIONS A. J. Clark School o Engneerng Department o Cvl and Envronmental Engneerng by Dr. Ibrahm A. Assakka Sprng 00 ENCE 03 - Computaton Methods n Cvl Engneerng II Department o

More information

: Numerical Analysis Topic 2: Solution of Nonlinear Equations Lectures 5-11:

: Numerical Analysis Topic 2: Solution of Nonlinear Equations Lectures 5-11: 764: Numercal Analyss Topc : Soluton o Nonlnear Equatons Lectures 5-: UIN Malang Read Chapters 5 and 6 o the tetbook 764_Topc Lecture 5 Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton

More information

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to ChE Lecture Notes - D. Keer, 5/9/98 Lecture 6,7,8 - Rootndng n systems o equatons (A) Theory (B) Problems (C) MATLAB Applcatons Tet: Supplementary notes rom Instructor 6. Why s t mportant to be able to

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

EE 330 Lecture 24. Small Signal Analysis Small Signal Analysis of BJT Amplifier

EE 330 Lecture 24. Small Signal Analysis Small Signal Analysis of BJT Amplifier EE 0 Lecture 4 Small Sgnal Analss Small Sgnal Analss o BJT Ampler Eam Frda March 9 Eam Frda Aprl Revew Sesson or Eam : 6:00 p.m. on Thursda March 8 n Room Sweene 6 Revew rom Last Lecture Comparson o Gans

More information

PART 8. Partial Differential Equations PDEs

PART 8. Partial Differential Equations PDEs he Islamc Unverst of Gaza Facult of Engneerng Cvl Engneerng Department Numercal Analss ECIV 3306 PAR 8 Partal Dfferental Equatons PDEs Chapter 9; Fnte Dfference: Ellptc Equatons Assocate Prof. Mazen Abualtaef

More information

CISE301: Numerical Methods Topic 2: Solution of Nonlinear Equations

CISE301: Numerical Methods Topic 2: Solution of Nonlinear Equations CISE3: Numercal Methods Topc : Soluton o Nonlnear Equatons Dr. Amar Khoukh Term Read Chapters 5 and 6 o the tetbook CISE3_Topc c Khoukh_ Lecture 5 Soluton o Nonlnear Equatons Root ndng Problems Dentons

More information

The Karush-Kuhn-Tucker. Nuno Vasconcelos ECE Department, UCSD

The Karush-Kuhn-Tucker. Nuno Vasconcelos ECE Department, UCSD e Karus-Kun-ucker condtons and dualt Nuno Vasconcelos ECE Department, UCSD Optmzaton goal: nd mamum or mnmum o a uncton Denton: gven unctons, g, 1,...,k and, 1,...m dened on some doman Ω R n mn w, w Ω

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Solution for singularly perturbed problems via cubic spline in tension

Solution for singularly perturbed problems via cubic spline in tension ISSN 76-769 England UK Journal of Informaton and Computng Scence Vol. No. 06 pp.6-69 Soluton for sngularly perturbed problems va cubc splne n tenson K. Aruna A. S. V. Rav Kant Flud Dynamcs Dvson Scool

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Introduction to Turbulence Modelling

Introduction to Turbulence Modelling Introdcton to Trblence Modellng 1 Nmercal methods 0 1 t Mathematcal descrpton p F Reslts For eample speed, pressre, temperatre Geometry Models for trblence, combston etc. Mathematcal descrpton of physcal

More information

HOMOGENEOUS LEAST SQUARES PROBLEM

HOMOGENEOUS LEAST SQUARES PROBLEM the Photogrammetrc Jornal of nl, Vol. 9, No., 005 HOMOGENEOU LE QURE PROLEM Kejo Inklä Helsnk Unversty of echnology Laboratory of Photogrammetry Remote ensng P.O.ox 00, IN-005 KK kejo.nkla@tkk.f RC he

More information

Lecture: Financing Based on Market Values II

Lecture: Financing Based on Market Values II Lectre: Fnancng Based on Market Vales II Ltz Krscwtz & Andreas Löler Dsconted Cas Flow, Secton 2.4.4 2.4.5, Otlne 2.4.4 Mles-Ezzell- and Modglan-Mller Mles-Ezzell adjstment Modglan-Mller adjstment 2.4.5

More information

Mathematical Economics MEMF e ME. Filomena Garcia. Topic 2 Calculus

Mathematical Economics MEMF e ME. Filomena Garcia. Topic 2 Calculus Mathematcal Economcs MEMF e ME Flomena Garca Topc 2 Calculus Mathematcal Economcs - www.seg.utl.pt/~garca/economa_matematca . Unvarate Calculus Calculus Functons : X Y y ( gves or each element X one element

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 umercal Solutons of oundary-value Problems n Os ovember 7, 7 umercal Solutons of oundary- Value Problems n Os Larry aretto Mechancal ngneerng 5 Semnar n ngneerng nalyss ovember 7, 7 Outlne Revew stff equaton

More information

A New Recursive Method for Solving State Equations Using Taylor Series

A New Recursive Method for Solving State Equations Using Taylor Series I J E E E C Internatonal Journal of Electrcal, Electroncs ISSN No. (Onlne) : 77-66 and Computer Engneerng 1(): -7(01) Specal Edton for Best Papers of Mcael Faraday IET Inda Summt-01, MFIIS-1 A New Recursve

More information

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or

More information

Grid Generation around a Cylinder by Complex Potential Functions

Grid Generation around a Cylinder by Complex Potential Functions Research Journal of Appled Scences, Engneerng and Technolog 4(): 53-535, 0 ISSN: 040-7467 Mawell Scentfc Organzaton, 0 Submtted: December 0, 0 Accepted: Januar, 0 Publshed: June 0, 0 Grd Generaton around

More information

Main components of the above cycle are: 1) Boiler (steam generator) heat exchanger 2) Turbine generates work 3) Condenser heat exchanger 4) Pump

Main components of the above cycle are: 1) Boiler (steam generator) heat exchanger 2) Turbine generates work 3) Condenser heat exchanger 4) Pump Introducton to Terodynacs, Lecture -5 Pro. G. Cccarell (0 Applcaton o Control olue Energy Analyss Most terodynac devces consst o a seres o coponents operatng n a cycle, e.g., stea power plant Man coponents

More information

2 Finite difference basics

2 Finite difference basics Numersche Methoden 1, WS 11/12 B.J.P. Kaus 2 Fnte dfference bascs Consder the one- The bascs of the fnte dfference method are best understood wth an example. dmensonal transent heat conducton equaton T

More information

Solutions to selected problems from homework 1.

Solutions to selected problems from homework 1. Jan Hagemejer 1 Soltons to selected problems from homeork 1. Qeston 1 Let be a tlty fncton hch generates demand fncton xp, ) and ndrect tlty fncton vp, ). Let F : R R be a strctly ncreasng fncton. If the

More information

Problem Set 1 Issued: Wednesday, February 11, 2015 Due: Monday, February 23, 2015

Problem Set 1 Issued: Wednesday, February 11, 2015 Due: Monday, February 23, 2015 MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE MASSACHUSETTS 09.9 NUMERICAL FLUID MECHANICS SPRING 05 Problem Set Issed: Wednesday Febrary 05 De: Monday Febrary 05

More information

MEMBRANE ELEMENT WITH NORMAL ROTATIONS

MEMBRANE ELEMENT WITH NORMAL ROTATIONS 9. MEMBRANE ELEMENT WITH NORMAL ROTATIONS Rotatons Mst Be Compatble Between Beam, Membrane and Shell Elements 9. INTRODUCTION { XE "Membrane Element" }The comple natre of most bldngs and other cvl engneerng

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

3/31/ = 0. φi φi. Use 10 Linear elements to solve the equation. dx x dx THE PETROV-GALERKIN METHOD

3/31/ = 0. φi φi. Use 10 Linear elements to solve the equation. dx x dx THE PETROV-GALERKIN METHOD THE PETROV-GAERKIN METHO Consder the Galern solton sng near elements of the modfed convecton-dffson eqaton α h d φ d φ + + = α s a parameter between and. If α =, we wll have the dscrete Galern form of

More information

CIVL 7/8111 Chapter 1 - Introduction to FEM 1/21

CIVL 7/8111 Chapter 1 - Introduction to FEM 1/21 CIV 7/8 Chapter - to FEM / The nte element method has become a powerl tool or the nmercal solton o a wde range o engneerng problems. Applcatons range rom deormaton and stress analyss o atomotve, arcrat,

More information

Complex Variables. Chapter 18 Integration in the Complex Plane. March 12, 2013 Lecturer: Shih-Yuan Chen

Complex Variables. Chapter 18 Integration in the Complex Plane. March 12, 2013 Lecturer: Shih-Yuan Chen omplex Varables hapter 8 Integraton n the omplex Plane March, Lecturer: Shh-Yuan hen Except where otherwse noted, content s lcensed under a BY-N-SA. TW Lcense. ontents ontour ntegrals auchy-goursat theorem

More information

, rst we solve te PDE's L ad L ad n g g (x) = ; = ; ; ; n () (x) = () Ten, we nd te uncton (x), te lnearzng eedbac and coordnates transormaton are gve

, rst we solve te PDE's L ad L ad n g g (x) = ; = ; ; ; n () (x) = () Ten, we nd te uncton (x), te lnearzng eedbac and coordnates transormaton are gve Freedom n Coordnates Transormaton or Exact Lnearzaton and ts Applcaton to Transent Beavor Improvement Kenj Fujmoto and Tosaru Suge Dvson o Appled Systems Scence, Kyoto Unversty, Uj, Kyoto, Japan suge@robotuassyoto-uacjp

More information

y A A B B d Vasos Pavlika (1) (2)

y A A B B d Vasos Pavlika (1) (2) The alclaton of smmetrc ct Geometres for Incompressble otatonal Flow Usng a fferental Eqaton pproach a ondar Integral Formla based on Green s Theorem Vasos Pavlka bstract In ths paper a nmercal algorthm

More information

Summary with Examples for Root finding Methods -Bisection -Newton Raphson -Secant

Summary with Examples for Root finding Methods -Bisection -Newton Raphson -Secant Summary wth Eamples or Root ndng Methods -Bsecton -Newton Raphson -Secant Nonlnear Equaton Solvers Bracketng Graphcal Open Methods Bsecton False Poston (Regula-Fals) Newton Raphson Secant All Iteratve

More information

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing i them?

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing i them? Image classfcaton Gven te bag-of-features representatons of mages from dfferent classes ow do we learn a model for dstngusng tem? Classfers Learn a decson rule assgnng bag-offeatures representatons of

More information

Anouncements. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers

Anouncements. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers Anouncements ultgrd Solvers The readng semnar starts ths week: o Usuall t wll e held n NEB 37 o Ths week t wll e n arland 3 chael Kazhdan (6657 ultgrd Solvers Recall: To compute the soluton to the osson

More information

Multigrid Methods and Applications in CFD

Multigrid Methods and Applications in CFD Multgrd Metods and Applcatons n CFD Mcael Wurst 0 May 009 Contents Introducton Typcal desgn of CFD solvers 3 Basc metods and ter propertes for solvng lnear systems of equatons 4 Geometrc Multgrd 3 5 Algebrac

More information

Complex Numbers Practice 0708 & SP 1. The complex number z is defined by

Complex Numbers Practice 0708 & SP 1. The complex number z is defined by IB Math Hgh Leel: Complex Nmbers Practce 0708 & SP Complex Nmbers Practce 0708 & SP. The complex nmber z s defned by π π π π z = sn sn. 6 6 Ale - Desert Academy (a) Express z n the form re, where r and

More information

5 The Laplace Equation in a convex polygon

5 The Laplace Equation in a convex polygon 5 Te Laplace Equaton n a convex polygon Te most mportant ellptc PDEs are te Laplace, te modfed Helmoltz and te Helmoltz equatons. Te Laplace equaton s u xx + u yy =. (5.) Te real and magnary parts of an

More information

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 493 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces you have studed thus far n the text are real vector spaces because the scalars

More information

Solving Singularly Perturbed Differential Difference Equations via Fitted Method

Solving Singularly Perturbed Differential Difference Equations via Fitted Method Avalable at ttp://pvamu.edu/aam Appl. Appl. Mat. ISSN: 193-9466 Vol. 8, Issue 1 (June 013), pp. 318-33 Applcatons and Appled Matematcs: An Internatonal Journal (AAM) Solvng Sngularly Perturbed Dfferental

More information

Mean Field / Variational Approximations

Mean Field / Variational Approximations Mean Feld / Varatonal Appromatons resented by Jose Nuñez 0/24/05 Outlne Introducton Mean Feld Appromaton Structured Mean Feld Weghted Mean Feld Varatonal Methods Introducton roblem: We have dstrbuton but

More information

SINGULARLY PERTURBED BOUNDARY VALUE. We consider the following singularly perturbed boundary value problem

SINGULARLY PERTURBED BOUNDARY VALUE. We consider the following singularly perturbed boundary value problem Cater PARAETRIC QUITIC SPLIE SOLUTIO OR SIGULARLY PERTURBED BOUDARY VALUE PROBLES Introcton We conser te ollong snglarly ertrbe bonary vale roblem " L r r > an ere s a small ostve arameter st

More information

Numerical Simulation of One-Dimensional Wave Equation by Non-Polynomial Quintic Spline

Numerical Simulation of One-Dimensional Wave Equation by Non-Polynomial Quintic Spline IOSR Journal of Matematcs (IOSR-JM) e-issn: 78-578, p-issn: 319-765X. Volume 14, Issue 6 Ver. I (Nov - Dec 018), PP 6-30 www.osrournals.org Numercal Smulaton of One-Dmensonal Wave Equaton by Non-Polynomal

More information

CS 3750 Machine Learning Lecture 6. Monte Carlo methods. CS 3750 Advanced Machine Learning. Markov chain Monte Carlo

CS 3750 Machine Learning Lecture 6. Monte Carlo methods. CS 3750 Advanced Machine Learning. Markov chain Monte Carlo CS 3750 Machne Learnng Lectre 6 Monte Carlo methods Mlos Haskrecht mlos@cs.ptt.ed 5329 Sennott Sqare Markov chan Monte Carlo Importance samplng: samples are generated accordng to Q and every sample from

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

2.29 Numerical Fluid Mechanics

2.29 Numerical Fluid Mechanics REVIEW Lecture 10: Sprng 2015 Lecture 11 Classfcaton of Partal Dfferental Equatons PDEs) and eamples wth fnte dfference dscretzatons Parabolc PDEs Ellptc PDEs Hyperbolc PDEs Error Types and Dscretzaton

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxaton Methods for Iteratve Soluton to Lnear Systems of Equatons Gerald Recktenwald Portland State Unversty Mechancal Engneerng Department gerry@pdx.edu Overvew Techncal topcs Basc Concepts Statonary

More information

SENSITIVITY APPROACH TO OPTIMAL CONTROL FOR AFFINE NONLINEAR DISCRETE-TIME SYSTEMS

SENSITIVITY APPROACH TO OPTIMAL CONTROL FOR AFFINE NONLINEAR DISCRETE-TIME SYSTEMS 448 Asan Journal o Control Vol. 7 No. 4 pp. 448-454 December 25 Bre Paper SENSIIVIY APPROACH O OPIMAL CONROL FOR AFFINE NONLINEAR DISCREE-IME SYSEMS Gong-You ang Nan Xe Peng Lu ABSRAC hs paper deals wth

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

TR/28. OCTOBER CUBIC SPLINE INTERPOLATION OF HARMONIC FUNCTIONS BY N. PAPAMICHAEL and J.R. WHITEMAN.

TR/28. OCTOBER CUBIC SPLINE INTERPOLATION OF HARMONIC FUNCTIONS BY N. PAPAMICHAEL and J.R. WHITEMAN. TR/8. OCTOBER 97. CUBIC SPLINE INTERPOLATION OF HARMONIC FUNCTIONS BY N. PAPAMICHAEL and J.R. WHITEMAN. W960748 ABSTRACT It s sown tat for te two dmensonal Laplace equaton a unvarate cubc splne approxmaton

More information

The Finite Element Method: A Short Introduction

The Finite Element Method: A Short Introduction Te Fnte Element Metod: A Sort ntroducton Wat s FEM? Te Fnte Element Metod (FEM) ntroduced by engneers n late 50 s and 60 s s a numercal tecnque for solvng problems wc are descrbed by Ordnary Dfferental

More information

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS Journal of Mathematcs and Statstcs 9 (1): 4-8, 1 ISSN 1549-644 1 Scence Publcatons do:1.844/jmssp.1.4.8 Publshed Onlne 9 (1) 1 (http://www.thescpub.com/jmss.toc) A MODIFIED METHOD FOR SOLVING SYSTEM OF

More information

Implicit Integration Henyey Method

Implicit Integration Henyey Method Implct Integraton Henyey Method In realstc stellar evoluton codes nstead of a drect ntegraton usng for example the Runge-Kutta method one employs an teratve mplct technque. Ths s because the structure

More information

Exact Solutions for Nonlinear D-S Equation by Two Known Sub-ODE Methods

Exact Solutions for Nonlinear D-S Equation by Two Known Sub-ODE Methods Internatonal Conference on Compter Technology and Scence (ICCTS ) IPCSIT vol. 47 () () IACSIT Press, Sngapore DOI:.7763/IPCSIT..V47.64 Exact Soltons for Nonlnear D-S Eqaton by Two Known Sb-ODE Methods

More information

Chapter 07: Kinetic Energy and Work

Chapter 07: Kinetic Energy and Work Chapter 07: Knetc Energy and Work Conservaton o Energy s one o Nature s undamental laws that s not volated. Energy can take on derent orms n a gven system. Ths chapter we wll dscuss work and knetc energy.

More information

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES COMPUTATIONAL FLUID DYNAMICS: FDM: Appromaton of Second Order Dervatves Lecture APPROXIMATION OF SECOMD ORDER DERIVATIVES. APPROXIMATION OF SECOND ORDER DERIVATIVES Second order dervatves appear n dffusve

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

C PLANE ELASTICITY PROBLEM FORMULATIONS

C PLANE ELASTICITY PROBLEM FORMULATIONS C M.. Tamn, CSMLab, UTM Corse Content: A ITRODUCTIO AD OVERVIEW mercal method and Compter-Aded Engneerng; Phscal problems; Mathematcal models; Fnte element method. B REVIEW OF -D FORMULATIOS Elements and

More information

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016 ME140 - Lnear rcuts - Wnter 16 Fnal, March 16, 2016 Instructons () The exam s open book. You may use your class notes and textbook. You may use a hand calculator wth no communcaton capabltes. () You have

More information

Experimental Errors and Error Analysis

Experimental Errors and Error Analysis Expermental Errors and Error Analss Rajee Prabhakar Unerst of Texas at Astn, Astn, TX Freeman Research Grop Meetng March 10, 004 Topcs coered Tpes of expermental errors Redcng errors Descrbng errors qanttatel

More information

Numerical Methods Solution of Nonlinear Equations

Numerical Methods Solution of Nonlinear Equations umercal Methods Soluton o onlnear Equatons Lecture Soluton o onlnear Equatons Root Fndng Prolems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods umercal Methods Bracketng Methods Open

More information

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222

More information

A Simple Research of Divisor Graphs

A Simple Research of Divisor Graphs The 29th Workshop on Combnatoral Mathematcs and Computaton Theory A Smple Research o Dvsor Graphs Yu-png Tsao General Educaton Center Chna Unversty o Technology Tape Tawan yp-tsao@cuteedutw Tape Tawan

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

More information

Lecture 22: Potential Energy

Lecture 22: Potential Energy Lecture : Potental Energy We have already studed the work-energy theorem, whch relates the total work done on an object to the change n knetc energy: Wtot = KE For a conservatve orce, the work done by

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

PHYS 1443 Section 004 Lecture #12 Thursday, Oct. 2, 2014

PHYS 1443 Section 004 Lecture #12 Thursday, Oct. 2, 2014 PHYS 1443 Secton 004 Lecture #1 Thursday, Oct., 014 Work-Knetc Energy Theorem Work under rcton Potental Energy and the Conservatve Force Gravtatonal Potental Energy Elastc Potental Energy Conservaton o

More information

A new Approach for Solving Linear Ordinary Differential Equations

A new Approach for Solving Linear Ordinary Differential Equations , ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Probability-Theoretic Junction Trees

Probability-Theoretic Junction Trees Probablty-Theoretc Juncton Trees Payam Pakzad, (wth Venkat Anantharam, EECS Dept, U.C. Berkeley EPFL, ALGO/LMA Semnar 2/2/2004 Margnalzaton Problem Gven an arbtrary functon of many varables, fnd (some

More information

Week 11: Chapter 11. The Vector Product. The Vector Product Defined. The Vector Product and Torque. More About the Vector Product

Week 11: Chapter 11. The Vector Product. The Vector Product Defined. The Vector Product and Torque. More About the Vector Product The Vector Product Week 11: Chapter 11 Angular Momentum There are nstances where the product of two vectors s another vector Earler we saw where the product of two vectors was a scalar Ths was called the

More information

GPU friendly Fast Poisson Solver for Structured Power Grid Network Analysis

GPU friendly Fast Poisson Solver for Structured Power Grid Network Analysis GPU frendly Fast Posson Solver for Strctred Power Grd Network Analyss Jn Sh, Yc Ca, Xaoy Wang Dept of Compter Scence and Technology, Tsngha Unv. Wentng Ho, Lwe Ma, Pe-Hsn Ho Synopsys Inc. Sheldon X.-D.

More information

P A = (P P + P )A = P (I P T (P P ))A = P (A P T (P P )A) Hence if we let E = P T (P P A), We have that

P A = (P P + P )A = P (I P T (P P ))A = P (A P T (P P )A) Hence if we let E = P T (P P A), We have that Backward Error Analyss for House holder Reectors We want to show that multplcaton by householder reectors s backward stable. In partcular we wsh to show fl(p A) = P (A) = P (A + E where P = I 2vv T s the

More information

A formally fourth-order accurate compact scheme for 3D Poisson equation in cylindrical and spherical coordinates

A formally fourth-order accurate compact scheme for 3D Poisson equation in cylindrical and spherical coordinates A formally fourth-order accurate compact scheme for 3D Posson equaton n cylndrcal and sphercal coordnates Mng-Chh La Ju-Mng Tseng Department of Appled Mathematcs Natonal Chao Tung Unversty Hsnchu 300,

More information

Problem Set 9 - Solutions Due: April 27, 2005

Problem Set 9 - Solutions Due: April 27, 2005 Problem Set - Solutons Due: Aprl 27, 2005. (a) Frst note that spam messages, nvtatons and other e-mal are all ndependent Posson processes, at rates pλ, qλ, and ( p q)λ. The event of the tme T at whch you

More information

Work is the change in energy of a system (neglecting heat transfer). To examine what could

Work is the change in energy of a system (neglecting heat transfer). To examine what could Work Work s the change n energy o a system (neglectng heat transer). To eamne what could cause work, let s look at the dmensons o energy: L ML E M L F L so T T dmensonally energy s equal to a orce tmes

More information

Chapter 5 Function-based Monte Carlo

Chapter 5 Function-based Monte Carlo Chapter 5 Functon-based Monte Carlo 5.1 Four technques or estmatng ntegrals Our net set o mathematcal tools that we wll develop nvolve Monte Carlo ntegraton. In the grand scheme o thngs, our study so ar

More information

CLOSED-FORM CHARACTERIZATION OF THE CHANNEL CAPACITY OF MULTI-BRANCH MAXIMAL RATIO COMBINING OVER CORRELATED NAKAGAMI FADING CHANNELS

CLOSED-FORM CHARACTERIZATION OF THE CHANNEL CAPACITY OF MULTI-BRANCH MAXIMAL RATIO COMBINING OVER CORRELATED NAKAGAMI FADING CHANNELS CLOSED-FORM CHARACTERIZATION OF THE CHANNEL CAPACITY OF MULTI-BRANCH MAXIMAL RATIO COMBINING OVER CORRELATED NAKAGAMI FADING CHANNELS Yawgeng A. Cha and Karl Yng-Ta Hang Department of Commncaton Engneerng,

More information

Numerical Simulation of Wave Propagation Using the Shallow Water Equations

Numerical Simulation of Wave Propagation Using the Shallow Water Equations umercal Smulaton of Wave Propagaton Usng the Shallow Water Equatons Junbo Par Harve udd College 6th Aprl 007 Abstract The shallow water equatons SWE were used to model water wave propagaton n one dmenson

More information

Chapter 5 rd Law of Thermodynamics

Chapter 5 rd Law of Thermodynamics Entropy and the nd and 3 rd Chapter 5 rd Law o hermodynamcs homas Engel, hlp Red Objectves Introduce entropy. Derve the condtons or spontanety. Show how S vares wth the macroscopc varables,, and. Chapter

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

EQUATION CHAPTER 1 SECTION 1STRAIN IN A CONTINUOUS MEDIUM

EQUATION CHAPTER 1 SECTION 1STRAIN IN A CONTINUOUS MEDIUM EQUTION HPTER SETION STRIN IN ONTINUOUS MEIUM ontent Introdcton One dmensonal stran Two-dmensonal stran Three-dmensonal stran ondtons for homogenety n two-dmensons n eample of deformaton of a lne Infntesmal

More information

Physics 2A Chapter 3 HW Solutions

Physics 2A Chapter 3 HW Solutions Phscs A Chapter 3 HW Solutons Chapter 3 Conceptual Queston: 4, 6, 8, Problems: 5,, 8, 7, 3, 44, 46, 69, 70, 73 Q3.4. Reason: (a) C = A+ B onl A and B are n the same drecton. Sze does not matter. (b) C

More information

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS)

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS) FE EIEW OPEATIONAL AMPLIFIES (OPAMPS) 1 The Opamp An opamp has two nputs and one output. Note the opamp below. The termnal labeled wth the () sgn s the nvertng nput and the nput labeled wth the () sgn

More information