which together show that the Lax-Milgram lemma can be applied. (c) We have the basic Galerkin orthogonality

Size: px
Start display at page:

Download "which together show that the Lax-Milgram lemma can be applied. (c) We have the basic Galerkin orthogonality"

Transcription

1 UPPSALA UNIVERSITY Departent of Inforation Technology Division of Scientific Coputing Solutions to exa in Finite eleent ethods II Stefan Engblo, Daniel Elfverson Question 1 Note: a inus sign in front of the A is issing on the exa. (a) Multiplying the PDE by a test-function v which vanishes at the boundary and integrating by parts, we get (A u, v) + (b u, v) + (cu, v) = (f, v). Define V = H 1 (Ω), the variational forulation reads: find u V such that a(u, v) =: (A u, v) + (b u, v) + (cu, v) = (f, v) := l(v), v V. For the Galerkin approxiation we consider a subspace V h V, find u h V h such that a(u h, v) = l(v), v V h. (b) The assuptions are (A u, u) a ( u, u), c 1 b > c >, and f L (Ω). Note also that We get = (n bu, 1) Ω = ( (bu ), 1) Ω = (( b)u, u) Ω + (b u, u) Ω. (1) a(u, u) = (A u, u) + (b u, u) + (cu, u) a ( u, u) + (cu, u) 1 (( b)u, u) a ( u, u) + c (u, u) = a u + c u in(a, c ) u V = u V, () for coercivity, a(u, v) = (A u, v) + (b u, v) + (cu, v) A L (Ω) u v + b L (Ω) u v + c L (Ω) u v C ab u v + C c u v C u V v V (3) for continuity, and l(v) = (f, v) = f v f v V, (4) which together show that the Lax-Milgra lea can be applied. (c) We have the basic Galerkin orthogonality a(u u h, v) = v V h. (5) We start by proving Cea s lea and then use the interpolation estiate u u h V 1 a(u u h, u u h ) = C a a(u u h, u v) We obtain for a Poincaré constant C p, C a u u h V u v V. (6) u u h V C a u v V C ac p (u πu) C ac p C π hin(,p) u H 3 (Ω). (7) 1

2 (d) First is to show that the solution the the variational proble iniize the functional F (v) = 1 a(v, v) l(v). We obtain F (u + v) = 1 a(u + v, u + v) F (u + v) = 1 (a(u, u) + a(u, v) + a(v, v)) l(u) l(v) = F (u) + a(u, v) l(u) + 1 a(v, v) F (u). (8) Then we ust show that a iniizer to F (v) solves the variational proble. Let g(ɛ) = F (u + ɛv), we obtain Differentiating g with respect the ɛ, we have g(ɛ) = 1 a(u + ɛv, u + ɛv) l(u + ɛv). (9) g (ɛ) = a(u, v) ɛa(v, v) l(v) (1) and setting ɛ = we have the variational forulation. Note that V h V, we can concludes the proof. Question (a) We have the syste 1 L 1 (1) L 1 (r) L 1 (s) L 1 (t) L 1 (rs) L 1 (rt) L 1 (st) L 1 (rst) c 1 L (1) L (r) L (s) L (t) L (rs) L (rt) L (st) L (rst) c L 3 (1) L 3 (r) L 3 (s) L 3 (t) L 3 (rs) L 3 (rt) L 3 (st) L 3 (rst) c 3 e 1 = = L 4 (1) L 4 (r) L 4 (s) L 4 (t) L 4 (rs) L 4 (rt) L 4 (st) L 4 (rst) c 4 L 5 (1) L 5 (r) L 5 (s) L 5 (t) L 5 (rs) L 5 (rt) L 5 (st) L 5 (rst) c 5 L 6 (1) L 6 (r) L 6 (s) L 6 (t) L 6 (rs) L 6 (rt) L 6 (st) L 6 (rst) c 6 L 7 (1) L 7 (r) L 7 (s) L 7 (t) L 7 (rs) L 7 (rt) L 7 (st) L 7 (rst) c 7 L 8 (1) L 8 (r) L 8 (s) L 8 (t) L 8 (rs) L 8 (rt) L 8 (st) L 8 (rst) c 8 (11) Which is 1 1 c c c 3 = 1 1 c c 5 (1) c c (b) We have u u h h u v h + v u h h (13) for v V h. For the first ter choose v as sup v Vh u v h. For the second ter we have v u h h 1 a h(v u h, v u h ) = 1 a h(v u + u u h, v u h ) c 8 = 1 (a h(v u, v u h ) + a h (u u h, v u h )) (14) = 1 (C α v u h v u h h + a h (u, v u h ) l(v u h ))

3 which iplies that v u h h 1 ( C α v u h + a ) h(u, v u h ) l(v u h ) v u h h 1 ( ) (15) a h (u, w) l(w) C α v u h + sup w V h Added the bound for the first and second ter we have for C = 1 + Cα. Question 3 u u h h (1 + C α ) inf u v h + 1 v V h sup a h (u, w) l(w) w V h ( ) (16) a h (u, w) l(w) C inf u v h + sup v V h w V h (a) Lets try to bound the gradient of the solution in ters of data. We have the variational for: find u V = H 1 (Ω) We obtain ɛ( u, v) + (b u, v) = (f, v) v V. (17) ɛ u = ɛ( u, u) + (b u, u) = (f, v) f v C P F f v. (18) where we used that = (n bu, 1) Ω = ( (bu ), 1) Ω = (( b)u, u) Ω + (b u, u) Ω. (19) together with b =. (b) First fro the continuous proble we have that u solves: find u V = H 1 (Ω) such that ɛ( u, v) + (b u, v) = (f, v) v V. () For the reaining part we have, ( ɛ u + b u, ɛ v + b v)) = (f, ɛ v + b v). (1) Also since, u H (Ω) V, we have that ɛ : H (Ω) L (Ω), but ɛ T : H 1 (T ) L (T ) is not true in general, so we can not test for all v V. The eleentwice operator ɛ T : V h L (T ) is true since all v V h are polynoials of finite diension. Multiplying the original equation with a test function in w L (Ω) defined as w T = ɛ v + b v L (T ) on eleent T, we have that (ɛ u, w) + (b u, w) = (f, w) ( ɛ u + b u, w) = (f, w), () for all v V h. Using the first and second part together we obtain that that u H V satisfies a h (u, v) = l(v) v V h. (3) 3

4 Question 4 (a) A typical Picard iteration for the proble is With u k as given we find by linearity that (c(u k ) u k+1 ) = f. (c(u k ) (u k u k+1 )) = r k, with hoogeneous Dirichlet boundary conditions. (b) The suggested iteration is clearly consistent since it is satisfied identically by any solution u to the PDE itself. Using the boundary conditions we readily find the variational forulation find u k+1 H 1 (Ω) such that for all v H 1 (Ω). This iplies the FEM ( v, a(u k ) u k+1 ) = (v, f) + ( v, b(u k ) u k ) A k ξ k+1 = b + B k ξ k, A k (i, j) = ( ϕ i, a(u k ) ϕ j ), B k (i, j) = ( ϕ i, b(u k ) ϕ j ), b i = (ϕ i, f), U k = j ξ k j ϕ j. (c) By taking v = u k+1 in the variational forulation we produce the energy estiate α u k+1 ( u k+1, a(u k ) u k+1 ) = (u k+1, f) + ( u k+1, b(u k ) u k ) u k+1 f + β u k+1 u k C f u k+1 + β u k+1 u k. Hence a suitable a priori bound is u k+1 α 1 C f + α 1 β u k, which if κ := α 1 β < 1 can be iterated to give α 1 C f + κα 1 C f + κ u k 1... (1 + κ κ k )α 1 C f + κ k+1 u α 1 C f 1 κ + u. (d) Here we show that the LM lea is applicable, but the proble is syetric so the RR theore is also applicable. We have l(v) = (v, f) + ( v, b(u k ) u k ) ax( f, β u k ) v H1 (Ω). a(u, v) = ( v, a(u k ) u) A k v u A k v H 1 (Ω) u H 1 (Ω), where a(u k ) A k (recall that a is bounded). Finally, a(u, u) α u α C u, = a(u, u) α in(1, 1/C ) u H 1 (Ω). 4

5 (e) We arrive at the Newton ethod by linearising around the previous iterate u k. Put u k+1 = u k +δ. Then this is achieved by a(u k+1 ) u k+1 = a(u k +δ) (u k +δ) = a(u k ) (u k + δ) + δa (u k ) u k + O(δ ). The variational forulation becoes find u k+1 H 1 (Ω) such that ( v, a(u k ) u k+1 ) + ( v, u k+1 a (u k ) u k ) = (v, f) + ( v, b(u k ) u k ) + ( v, u k a (u k ) u k ) for all v H 1 (Ω). The FEM becoes with atrices A and B as before and with (A k + C k )ξ k+1 = b + (B k + C k )ξ k, C k (i, j) = ( ϕ i, ϕ j a (U k ) U k ). 5

WELL POSEDNESS OF PROBLEMS I

WELL POSEDNESS OF PROBLEMS I Finite Element Method 85 WELL POSEDNESS OF PROBLEMS I Consider the following generic problem Lu = f, where L : X Y, u X, f Y and X, Y are two Banach spaces We say that the above problem is well-posed (according

More information

arxiv: v1 [physics.comp-ph] 20 Oct 2017 Abstract

arxiv: v1 [physics.comp-ph] 20 Oct 2017 Abstract Convergence Properties & Extension to Non-Conservative Multidiensional ystes Haran Jackson Cavendish Laboratory, JJ Thoson Ave, Cabridge, UK, CB3 0HE arxiv:70.07603v [physics.cop-ph] 20 Oct 207 Abstract

More information

Introduction to Discrete Optimization

Introduction to Discrete Optimization Prof. Friedrich Eisenbrand Martin Nieeier Due Date: March 9 9 Discussions: March 9 Introduction to Discrete Optiization Spring 9 s Exercise Consider a school district with I neighborhoods J schools and

More information

Konrad-Zuse-Zentrum für Informationstechnik Berlin Heilbronner Str. 10, D Berlin - Wilmersdorf

Konrad-Zuse-Zentrum für Informationstechnik Berlin Heilbronner Str. 10, D Berlin - Wilmersdorf Konrad-Zuse-Zentru für Inforationstechnik Berlin Heilbronner Str. 10, D-10711 Berlin - Wilersdorf Folkar A. Borneann On the Convergence of Cascadic Iterations for Elliptic Probles SC 94-8 (Marz 1994) 1

More information

RECOVERY OF A DENSITY FROM THE EIGENVALUES OF A NONHOMOGENEOUS MEMBRANE

RECOVERY OF A DENSITY FROM THE EIGENVALUES OF A NONHOMOGENEOUS MEMBRANE Proceedings of ICIPE rd International Conference on Inverse Probles in Engineering: Theory and Practice June -8, 999, Port Ludlow, Washington, USA : RECOVERY OF A DENSITY FROM THE EIGENVALUES OF A NONHOMOGENEOUS

More information

ON THE TWO-LEVEL PRECONDITIONING IN LEAST SQUARES METHOD

ON THE TWO-LEVEL PRECONDITIONING IN LEAST SQUARES METHOD PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical and Matheatical Sciences 04,, p. 7 5 ON THE TWO-LEVEL PRECONDITIONING IN LEAST SQUARES METHOD M a t h e a t i c s Yu. A. HAKOPIAN, R. Z. HOVHANNISYAN

More information

RESTARTED FULL ORTHOGONALIZATION METHOD FOR SHIFTED LINEAR SYSTEMS

RESTARTED FULL ORTHOGONALIZATION METHOD FOR SHIFTED LINEAR SYSTEMS BIT Nuerical Matheatics 43: 459 466, 2003. 2003 Kluwer Acadeic Publishers. Printed in The Netherlands 459 RESTARTED FULL ORTHOGONALIZATION METHOD FOR SHIFTED LINEAR SYSTEMS V. SIMONCINI Dipartiento di

More information

Kinetic Theory of Gases: Elementary Ideas

Kinetic Theory of Gases: Elementary Ideas Kinetic Theory of Gases: Eleentary Ideas 17th February 2010 1 Kinetic Theory: A Discussion Based on a Siplified iew of the Motion of Gases 1.1 Pressure: Consul Engel and Reid Ch. 33.1) for a discussion

More information

Supplementary Material for Fast and Provable Algorithms for Spectrally Sparse Signal Reconstruction via Low-Rank Hankel Matrix Completion

Supplementary Material for Fast and Provable Algorithms for Spectrally Sparse Signal Reconstruction via Low-Rank Hankel Matrix Completion Suppleentary Material for Fast and Provable Algoriths for Spectrally Sparse Signal Reconstruction via Low-Ran Hanel Matrix Copletion Jian-Feng Cai Tianing Wang Ke Wei March 1, 017 Abstract We establish

More information

The Stefan problem with moving boundary. Key Words: Stefan Problem; Crank-Nicolson method; Moving Boundary; Finite Element Method.

The Stefan problem with moving boundary. Key Words: Stefan Problem; Crank-Nicolson method; Moving Boundary; Finite Element Method. Bol. Soc. Paran. Mat. 3s. v. 6-8: 9 6. c SPM ISNN-3787 The Stefan proble with oving boundary M. A. Rincon & A. Scardua abstract: A atheatical odel of the linear therodynaic equations with oving ends, based

More information

Approximation in Banach Spaces by Galerkin Methods

Approximation in Banach Spaces by Galerkin Methods 2 Approximation in Banach Spaces by Galerkin Methods In this chapter, we consider an abstract linear problem which serves as a generic model for engineering applications. Our first goal is to specify the

More information

Kinetic Theory of Gases: Elementary Ideas

Kinetic Theory of Gases: Elementary Ideas Kinetic Theory of Gases: Eleentary Ideas 9th February 011 1 Kinetic Theory: A Discussion Based on a Siplified iew of the Motion of Gases 1.1 Pressure: Consul Engel and Reid Ch. 33.1) for a discussion of

More information

Lecture 21. Interior Point Methods Setup and Algorithm

Lecture 21. Interior Point Methods Setup and Algorithm Lecture 21 Interior Point Methods In 1984, Kararkar introduced a new weakly polynoial tie algorith for solving LPs [Kar84a], [Kar84b]. His algorith was theoretically faster than the ellipsoid ethod and

More information

A Simple Regression Problem

A Simple Regression Problem A Siple Regression Proble R. M. Castro March 23, 2 In this brief note a siple regression proble will be introduced, illustrating clearly the bias-variance tradeoff. Let Y i f(x i ) + W i, i,..., n, where

More information

Soft-margin SVM can address linearly separable problems with outliers

Soft-margin SVM can address linearly separable problems with outliers Non-linear Support Vector Machines Non-linearly separable probles Hard-argin SVM can address linearly separable probles Soft-argin SVM can address linearly separable probles with outliers Non-linearly

More information

New upper bound for the B-spline basis condition number II. K. Scherer. Institut fur Angewandte Mathematik, Universitat Bonn, Bonn, Germany.

New upper bound for the B-spline basis condition number II. K. Scherer. Institut fur Angewandte Mathematik, Universitat Bonn, Bonn, Germany. New upper bound for the B-spline basis condition nuber II. A proof of de Boor's 2 -conjecture K. Scherer Institut fur Angewandte Matheati, Universitat Bonn, 535 Bonn, Gerany and A. Yu. Shadrin Coputing

More information

Support recovery in compressed sensing: An estimation theoretic approach

Support recovery in compressed sensing: An estimation theoretic approach Support recovery in copressed sensing: An estiation theoretic approach Ain Karbasi, Ali Horati, Soheil Mohajer, Martin Vetterli School of Coputer and Counication Sciences École Polytechnique Fédérale de

More information

Regularity Theory a Fourth Order PDE with Delta Right Hand Side

Regularity Theory a Fourth Order PDE with Delta Right Hand Side Regularity Theory a Fourth Order PDE with Delta Right Hand Side Graham Hobbs Applied PDEs Seminar, 29th October 2013 Contents Problem and Weak Formulation Example - The Biharmonic Problem Regularity Theory

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fitting of Data David Eberly, Geoetric Tools, Redond WA 98052 https://www.geoetrictools.co/ This work is licensed under the Creative Coons Attribution 4.0 International License. To view a

More information

Linear Algebra (I) Yijia Chen. linear transformations and their algebraic properties. 1. A Starting Point. y := 3x.

Linear Algebra (I) Yijia Chen. linear transformations and their algebraic properties. 1. A Starting Point. y := 3x. Linear Algebra I) Yijia Chen Linear algebra studies Exaple.. Consider the function This is a linear function f : R R. linear transforations and their algebraic properties.. A Starting Point y := 3x. Geoetrically

More information

Concentration of ground states in stationary Mean-Field Games systems: part I

Concentration of ground states in stationary Mean-Field Games systems: part I Concentration of ground states in stationary Mean-Field Gaes systes: part I Annalisa Cesaroni and Marco Cirant June 9, 207 Abstract In this paper we provide the existence of classical solutions to soe

More information

ANALYSIS OF RECOVERY TYPE A POSTERIORI ERROR ESTIMATORS FOR MILDLY STRUCTURED GRIDS

ANALYSIS OF RECOVERY TYPE A POSTERIORI ERROR ESTIMATORS FOR MILDLY STRUCTURED GRIDS MATHEMATICS OF COMPUTATION Volue 73, Nuber 247, Pages 1139 1152 S 0025-5718(03)01600-4 Article electronically published on August 19, 2003 ANALYSIS OF RECOVERY TYPE A POSTERIORI ERROR ESTIMATORS FOR MILDLY

More information

Explicit solution of the polynomial least-squares approximation problem on Chebyshev extrema nodes

Explicit solution of the polynomial least-squares approximation problem on Chebyshev extrema nodes Explicit solution of the polynoial least-squares approxiation proble on Chebyshev extrea nodes Alfredo Eisinberg, Giuseppe Fedele Dipartiento di Elettronica Inforatica e Sisteistica, Università degli Studi

More information

THE POLYNOMIAL REPRESENTATION OF THE TYPE A n 1 RATIONAL CHEREDNIK ALGEBRA IN CHARACTERISTIC p n

THE POLYNOMIAL REPRESENTATION OF THE TYPE A n 1 RATIONAL CHEREDNIK ALGEBRA IN CHARACTERISTIC p n THE POLYNOMIAL REPRESENTATION OF THE TYPE A n RATIONAL CHEREDNIK ALGEBRA IN CHARACTERISTIC p n SHEELA DEVADAS AND YI SUN Abstract. We study the polynoial representation of the rational Cherednik algebra

More information

A Bernstein-Markov Theorem for Normed Spaces

A Bernstein-Markov Theorem for Normed Spaces A Bernstein-Markov Theore for Nored Spaces Lawrence A. Harris Departent of Matheatics, University of Kentucky Lexington, Kentucky 40506-0027 Abstract Let X and Y be real nored linear spaces and let φ :

More information

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation Course Notes for EE7C (Spring 018: Convex Optiization and Approxiation Instructor: Moritz Hardt Eail: hardt+ee7c@berkeley.edu Graduate Instructor: Max Sichowitz Eail: sichow+ee7c@berkeley.edu October 15,

More information

A new type of lower bound for the largest eigenvalue of a symmetric matrix

A new type of lower bound for the largest eigenvalue of a symmetric matrix Linear Algebra and its Applications 47 7 9 9 www.elsevier.co/locate/laa A new type of lower bound for the largest eigenvalue of a syetric atrix Piet Van Mieghe Delft University of Technology, P.O. Box

More information

Page 1 Lab 1 Elementary Matrix and Linear Algebra Spring 2011

Page 1 Lab 1 Elementary Matrix and Linear Algebra Spring 2011 Page Lab Eleentary Matri and Linear Algebra Spring 0 Nae Due /03/0 Score /5 Probles through 4 are each worth 4 points.. Go to the Linear Algebra oolkit site ransforing a atri to reduced row echelon for

More information

Block designs and statistics

Block designs and statistics Bloc designs and statistics Notes for Math 447 May 3, 2011 The ain paraeters of a bloc design are nuber of varieties v, bloc size, nuber of blocs b. A design is built on a set of v eleents. Each eleent

More information

Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon

Model Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon Model Fitting CURM Background Material, Fall 014 Dr. Doreen De Leon 1 Introduction Given a set of data points, we often want to fit a selected odel or type to the data (e.g., we suspect an exponential

More information

A posteriori error estimation for elliptic problems

A posteriori error estimation for elliptic problems A posteriori error estimation for elliptic problems Praveen. C praveen@math.tifrbng.res.in Tata Institute of Fundamental Research Center for Applicable Mathematics Bangalore 560065 http://math.tifrbng.res.in

More information

Introduction to Robotics (CS223A) (Winter 2006/2007) Homework #5 solutions

Introduction to Robotics (CS223A) (Winter 2006/2007) Homework #5 solutions Introduction to Robotics (CS3A) Handout (Winter 6/7) Hoework #5 solutions. (a) Derive a forula that transfors an inertia tensor given in soe frae {C} into a new frae {A}. The frae {A} can differ fro frae

More information

From Completing the Squares and Orthogonal Projection to Finite Element Methods

From Completing the Squares and Orthogonal Projection to Finite Element Methods From Completing the Squares and Orthogonal Projection to Finite Element Methods Mo MU Background In scientific computing, it is important to start with an appropriate model in order to design effective

More information

The Solution of One-Phase Inverse Stefan Problem. by Homotopy Analysis Method

The Solution of One-Phase Inverse Stefan Problem. by Homotopy Analysis Method Applied Matheatical Sciences, Vol. 8, 214, no. 53, 2635-2644 HIKARI Ltd, www.-hikari.co http://dx.doi.org/1.12988/as.214.43152 The Solution of One-Phase Inverse Stefan Proble by Hootopy Analysis Method

More information

On the Use of A Priori Information for Sparse Signal Approximations

On the Use of A Priori Information for Sparse Signal Approximations ITS TECHNICAL REPORT NO. 3/4 On the Use of A Priori Inforation for Sparse Signal Approxiations Oscar Divorra Escoda, Lorenzo Granai and Pierre Vandergheynst Signal Processing Institute ITS) Ecole Polytechnique

More information

ANALYSIS OF A FULLY DISCRETE FINITE ELEMENT METHOD FOR THE PHASE FIELD MODEL AND APPROXIMATION OF ITS SHARP INTERFACE LIMITS

ANALYSIS OF A FULLY DISCRETE FINITE ELEMENT METHOD FOR THE PHASE FIELD MODEL AND APPROXIMATION OF ITS SHARP INTERFACE LIMITS ANALYSIS OF A FULLY DISCRETE FINITE ELEMENT METHOD FOR THE PHASE FIELD MODEL AND APPROXIMATION OF ITS SHARP INTERFACE LIMITS XIAOBING FENG AND ANDREAS PROHL Abstract. We propose and analyze a fully discrete

More information

. The univariate situation. It is well-known for a long tie that denoinators of Pade approxiants can be considered as orthogonal polynoials with respe

. The univariate situation. It is well-known for a long tie that denoinators of Pade approxiants can be considered as orthogonal polynoials with respe PROPERTIES OF MULTIVARIATE HOMOGENEOUS ORTHOGONAL POLYNOMIALS Brahi Benouahane y Annie Cuyt? Keywords Abstract It is well-known that the denoinators of Pade approxiants can be considered as orthogonal

More information

Numerical Methods for Elliptic Partial Differential Equations with Random Coefficients

Numerical Methods for Elliptic Partial Differential Equations with Random Coefficients MSC MATHEMATICS MASTER THESIS Nuerical Methods for Elliptic Partial ifferential Equations with Rando Coefficients Author: Chiara Pizzigoni Supervisor: dr. S.G. Cox prof. dr. R.P. Stevenson Exaination date:

More information

OPTIMAL DESIGNS FOR ESTIMATING PAIRS OF COEFFICIENTS IN FOURIER REGRESSION MODELS

OPTIMAL DESIGNS FOR ESTIMATING PAIRS OF COEFFICIENTS IN FOURIER REGRESSION MODELS Statistica Sinica 19 2009, 1587-1601 OPTIMAL DESIGNS FOR ESTIMATING PAIRS OF COEFFICIENTS IN FOURIER REGRESSION MODELS Holger Dette 1, Viatcheslav B. Melas 2 and Petr Shpilev 2 1 Ruhr-Universität Bochu

More information

On the Communication Complexity of Lipschitzian Optimization for the Coordinated Model of Computation

On the Communication Complexity of Lipschitzian Optimization for the Coordinated Model of Computation journal of coplexity 6, 459473 (2000) doi:0.006jco.2000.0544, available online at http:www.idealibrary.co on On the Counication Coplexity of Lipschitzian Optiization for the Coordinated Model of Coputation

More information

NONLINEAR KIRCHHOFF-CARRIER WAVE EQUATION IN A UNIT MEMBRANE WITH MIXED HOMOGENEOUS BOUNDARY CONDITIONS

NONLINEAR KIRCHHOFF-CARRIER WAVE EQUATION IN A UNIT MEMBRANE WITH MIXED HOMOGENEOUS BOUNDARY CONDITIONS Electronic Journal of Differential Equations, Vol. 2525, No. 138, pp. 1 18. ISSN: 172-6691. URL: http://ejde.ath.txstate.edu or http://ejde.ath.unt.edu ftp ejde.ath.txstate.edu login: ftp NONLINEAR KIRCHHOFF-CARRIER

More information

Supporting Information for Supression of Auger Processes in Confined Structures

Supporting Information for Supression of Auger Processes in Confined Structures Supporting Inforation for Supression of Auger Processes in Confined Structures George E. Cragg and Alexander. Efros Naval Research aboratory, Washington, DC 20375, USA 1 Solution of the Coupled, Two-band

More information

Robustness and Regularization of Support Vector Machines

Robustness and Regularization of Support Vector Machines Robustness and Regularization of Support Vector Machines Huan Xu ECE, McGill University Montreal, QC, Canada xuhuan@ci.cgill.ca Constantine Caraanis ECE, The University of Texas at Austin Austin, TX, USA

More information

arxiv: v1 [math.na] 10 Oct 2016

arxiv: v1 [math.na] 10 Oct 2016 GREEDY GAUSS-NEWTON ALGORITHM FOR FINDING SPARSE SOLUTIONS TO NONLINEAR UNDERDETERMINED SYSTEMS OF EQUATIONS MÅRTEN GULLIKSSON AND ANNA OLEYNIK arxiv:6.395v [ath.na] Oct 26 Abstract. We consider the proble

More information

A Theoretical Analysis of a Warm Start Technique

A Theoretical Analysis of a Warm Start Technique A Theoretical Analysis of a War Start Technique Martin A. Zinkevich Yahoo! Labs 701 First Avenue Sunnyvale, CA Abstract Batch gradient descent looks at every data point for every step, which is wasteful

More information

1 Bounding the Margin

1 Bounding the Margin COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #12 Scribe: Jian Min Si March 14, 2013 1 Bounding the Margin We are continuing the proof of a bound on the generalization error of AdaBoost

More information

HESSIAN MATRICES OF PENALTY FUNCTIONS FOR SOLVING CONSTRAINED-OPTIMIZATION PROBLEMS

HESSIAN MATRICES OF PENALTY FUNCTIONS FOR SOLVING CONSTRAINED-OPTIMIZATION PROBLEMS R 702 Philips Res. Repts 24, 322-330, 1969 HESSIAN MATRICES OF PENALTY FUNCTIONS FOR SOLVING CONSTRAINED-OPTIMIZATION PROBLEMS by F. A. LOOTSMA Abstract This paper deals with the Hessian atrices of penalty

More information

Support Vector Machines. Maximizing the Margin

Support Vector Machines. Maximizing the Margin Support Vector Machines Support vector achines (SVMs) learn a hypothesis: h(x) = b + Σ i= y i α i k(x, x i ) (x, y ),..., (x, y ) are the training exs., y i {, } b is the bias weight. α,..., α are the

More information

Calculation of load limits by the method of Norton- Hoff-Friaâ, behavior NORTON_HOFF

Calculation of load limits by the method of Norton- Hoff-Friaâ, behavior NORTON_HOFF Titre : Calcul de charge liite par la éthode de Norton-H[...] Date : 25/02/2014 Page : 1/12 Calculation of load liits by the ethod of Norton- Hoff-Friaâ, behavior NORTON_HOFF Suary: The liiting analysis

More information

The Methods of Solution for Constrained Nonlinear Programming

The Methods of Solution for Constrained Nonlinear Programming Research Inventy: International Journal Of Engineering And Science Vol.4, Issue 3(March 2014), PP 01-06 Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.co The Methods of Solution for Constrained

More information

Bulletin of the. Iranian Mathematical Society

Bulletin of the. Iranian Mathematical Society ISSN: 1017-060X (Print) ISSN: 1735-8515 (Online) Bulletin of the Iranian Matheatical Society Vol. 41 (2015), No. 4, pp. 981 1001. Title: Convergence analysis of the global FOM and GMRES ethods for solving

More information

ANALYSIS OF A NUMERICAL SOLVER FOR RADIATIVE TRANSPORT EQUATION

ANALYSIS OF A NUMERICAL SOLVER FOR RADIATIVE TRANSPORT EQUATION ANALYSIS OF A NUMERICAL SOLVER FOR RADIATIVE TRANSPORT EQUATION HAO GAO AND HONGKAI ZHAO Abstract. We analyze a nuerical algorith for solving radiative transport equation with vacuu or reflection boundary

More information

arxiv:math/ v1 [math.nt] 15 Jul 2003

arxiv:math/ v1 [math.nt] 15 Jul 2003 arxiv:ath/0307203v [ath.nt] 5 Jul 2003 A quantitative version of the Roth-Ridout theore Toohiro Yaada, 606-8502, Faculty of Science, Kyoto University, Kitashirakawaoiwakecho, Sakyoku, Kyoto-City, Kyoto,

More information

Optimal quantum detectors for unambiguous detection of mixed states

Optimal quantum detectors for unambiguous detection of mixed states PHYSICAL REVIEW A 69, 06318 (004) Optial quantu detectors for unabiguous detection of ixed states Yonina C. Eldar* Departent of Electrical Engineering, Technion Israel Institute of Technology, Haifa 3000,

More information

Chapter 6 1-D Continuous Groups

Chapter 6 1-D Continuous Groups Chapter 6 1-D Continuous Groups Continuous groups consist of group eleents labelled by one or ore continuous variables, say a 1, a 2,, a r, where each variable has a well- defined range. This chapter explores:

More information

i ij j ( ) sin cos x y z x x x interchangeably.)

i ij j ( ) sin cos x y z x x x interchangeably.) Tensor Operators Michael Fowler,2/3/12 Introduction: Cartesian Vectors and Tensors Physics is full of vectors: x, L, S and so on Classically, a (three-diensional) vector is defined by its properties under

More information

An introduction to the mathematical theory of finite elements

An introduction to the mathematical theory of finite elements Master in Seismic Engineering E.T.S.I. Industriales (U.P.M.) Discretization Methods in Engineering An introduction to the mathematical theory of finite elements Ignacio Romero ignacio.romero@upm.es October

More information

Anisotropic inverse conductivity and scattering problems

Anisotropic inverse conductivity and scattering problems Anisotropic inverse conductivity and scattering probles Kiwoon Kwon Dongwoo Sheen To appear in Inverse Probles, 00 Abstract Uniqueness in inverse conductivity and scattering probles is considered. In case

More information

Scientific Computing WS 2018/2019. Lecture 15. Jürgen Fuhrmann Lecture 15 Slide 1

Scientific Computing WS 2018/2019. Lecture 15. Jürgen Fuhrmann Lecture 15 Slide 1 Scientific Computing WS 2018/2019 Lecture 15 Jürgen Fuhrmann juergen.fuhrmann@wias-berlin.de Lecture 15 Slide 1 Lecture 15 Slide 2 Problems with strong formulation Writing the PDE with divergence and gradient

More information

Quantum Chemistry Exam 2 Take-home Solutions

Quantum Chemistry Exam 2 Take-home Solutions Cheistry 60 Fall 07 Dr Jean M Standard Nae KEY Quantu Cheistry Exa Take-hoe Solutions 5) (0 points) In this proble, the nonlinear variation ethod will be used to deterine an approxiate solution for the

More information

13.2 Fully Polynomial Randomized Approximation Scheme for Permanent of Random 0-1 Matrices

13.2 Fully Polynomial Randomized Approximation Scheme for Permanent of Random 0-1 Matrices CS71 Randoness & Coputation Spring 018 Instructor: Alistair Sinclair Lecture 13: February 7 Disclaier: These notes have not been subjected to the usual scrutiny accorded to foral publications. They ay

More information

1. Introduction. This paper is concerned with the study of convergence of Krylov subspace methods for solving linear systems of equations,

1. Introduction. This paper is concerned with the study of convergence of Krylov subspace methods for solving linear systems of equations, ANALYSIS OF SOME KRYLOV SUBSPACE METODS FOR NORMAL MATRICES VIA APPROXIMATION TEORY AND CONVEX OPTIMIZATION M BELLALIJ, Y SAAD, AND SADOK Dedicated to Gerard Meurant at the occasion of his 60th birthday

More information

Supplementary Materials: Proofs and Technical Details for Parsimonious Tensor Response Regression Lexin Li and Xin Zhang

Supplementary Materials: Proofs and Technical Details for Parsimonious Tensor Response Regression Lexin Li and Xin Zhang Suppleentary Materials: Proofs and Tecnical Details for Parsionious Tensor Response Regression Lexin Li and Xin Zang A Soe preliinary results We will apply te following two results repeatedly. For a positive

More information

1 Discretizing BVP with Finite Element Methods.

1 Discretizing BVP with Finite Element Methods. 1 Discretizing BVP with Finite Element Methods In this section, we will discuss a process for solving boundary value problems numerically, the Finite Element Method (FEM) We note that such method is a

More information

Finite difference method for elliptic problems: I

Finite difference method for elliptic problems: I Finite difference method for elliptic problems: I Praveen. C praveen@math.tifrbng.res.in Tata Institute of Fundamental Research Center for Applicable Mathematics Bangalore 560065 http://math.tifrbng.res.in/~praveen

More information

Shannon Sampling II. Connections to Learning Theory

Shannon Sampling II. Connections to Learning Theory Shannon Sapling II Connections to Learning heory Steve Sale oyota echnological Institute at Chicago 147 East 60th Street, Chicago, IL 60637, USA E-ail: sale@athberkeleyedu Ding-Xuan Zhou Departent of Matheatics,

More information

Semicircle law for generalized Curie-Weiss matrix ensembles at subcritical temperature

Semicircle law for generalized Curie-Weiss matrix ensembles at subcritical temperature Seicircle law for generalized Curie-Weiss atrix ensebles at subcritical teperature Werner Kirsch Fakultät für Matheatik und Inforatik FernUniversität in Hagen, Gerany Thoas Kriecherbauer Matheatisches

More information

Consistent Multiclass Algorithms for Complex Performance Measures. Supplementary Material

Consistent Multiclass Algorithms for Complex Performance Measures. Supplementary Material Consistent Multiclass Algoriths for Coplex Perforance Measures Suppleentary Material Notations. Let λ be the base easure over n given by the unifor rando variable (say U over n. Hence, for all easurable

More information

Hybrid System Identification: An SDP Approach

Hybrid System Identification: An SDP Approach 49th IEEE Conference on Decision and Control Deceber 15-17, 2010 Hilton Atlanta Hotel, Atlanta, GA, USA Hybrid Syste Identification: An SDP Approach C Feng, C M Lagoa, N Ozay and M Sznaier Abstract The

More information

POD-DEIM MODEL ORDER REDUCTION FOR THE MONODOMAIN REACTION-DIFFUSION EQUATION IN NEURO-MUSCULAR SYSTEM

POD-DEIM MODEL ORDER REDUCTION FOR THE MONODOMAIN REACTION-DIFFUSION EQUATION IN NEURO-MUSCULAR SYSTEM 6th European Conference on Coputational Mechanics (ECCM 6) 7th European Conference on Coputational Fluid Dynaics (ECFD 7) 1115 June 2018, Glasgow, UK POD-DEIM MODEL ORDER REDUCTION FOR THE MONODOMAIN REACTION-DIFFUSION

More information

ABSTRACT INTRODUCTION

ABSTRACT INTRODUCTION Wave Resistance Prediction of a Cataaran by Linearised Theory M.INSEL Faculty of Naval Architecture and Ocean Engineering, Istanbul Technical University, TURKEY A.F.MOLLAND, J.F.WELLICOME Departent of

More information

AN APPLICATION OF CUBIC B-SPLINE FINITE ELEMENT METHOD FOR THE BURGERS EQUATION

AN APPLICATION OF CUBIC B-SPLINE FINITE ELEMENT METHOD FOR THE BURGERS EQUATION Aksan, E..: An Applıcatıon of Cubıc B-Splıne Fınıte Eleent Method for... THERMAL SCIECE: Year 8, Vol., Suppl., pp. S95-S S95 A APPLICATIO OF CBIC B-SPLIE FIITE ELEMET METHOD FOR THE BRGERS EQATIO by Eine

More information

A Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness

A Note on Scheduling Tall/Small Multiprocessor Tasks with Unit Processing Time to Minimize Maximum Tardiness A Note on Scheduling Tall/Sall Multiprocessor Tasks with Unit Processing Tie to Miniize Maxiu Tardiness Philippe Baptiste and Baruch Schieber IBM T.J. Watson Research Center P.O. Box 218, Yorktown Heights,

More information

The Weierstrass Approximation Theorem

The Weierstrass Approximation Theorem 36 The Weierstrass Approxiation Theore Recall that the fundaental idea underlying the construction of the real nubers is approxiation by the sipler rational nubers. Firstly, nubers are often deterined

More information

CS Lecture 13. More Maximum Likelihood

CS Lecture 13. More Maximum Likelihood CS 6347 Lecture 13 More Maxiu Likelihood Recap Last tie: Introduction to axiu likelihood estiation MLE for Bayesian networks Optial CPTs correspond to epirical counts Today: MLE for CRFs 2 Maxiu Likelihood

More information

Polygonal Designs: Existence and Construction

Polygonal Designs: Existence and Construction Polygonal Designs: Existence and Construction John Hegean Departent of Matheatics, Stanford University, Stanford, CA 9405 Jeff Langford Departent of Matheatics, Drake University, Des Moines, IA 5011 G

More information

A note on the realignment criterion

A note on the realignment criterion A note on the realignent criterion Chi-Kwong Li 1, Yiu-Tung Poon and Nung-Sing Sze 3 1 Departent of Matheatics, College of Willia & Mary, Williasburg, VA 3185, USA Departent of Matheatics, Iowa State University,

More information

Complex Quadratic Optimization and Semidefinite Programming

Complex Quadratic Optimization and Semidefinite Programming Coplex Quadratic Optiization and Seidefinite Prograing Shuzhong Zhang Yongwei Huang August 4 Abstract In this paper we study the approxiation algoriths for a class of discrete quadratic optiization probles

More information

FAST DYNAMO ON THE REAL LINE

FAST DYNAMO ON THE REAL LINE FAST DYAMO O THE REAL LIE O. KOZLOVSKI & P. VYTOVA Abstract. In this paper we show that a piecewise expanding ap on the interval, extended to the real line by a non-expanding ap satisfying soe ild hypthesis

More information

Scientific Computing WS 2017/2018. Lecture 18. Jürgen Fuhrmann Lecture 18 Slide 1

Scientific Computing WS 2017/2018. Lecture 18. Jürgen Fuhrmann Lecture 18 Slide 1 Scientific Computing WS 2017/2018 Lecture 18 Jürgen Fuhrmann juergen.fuhrmann@wias-berlin.de Lecture 18 Slide 1 Lecture 18 Slide 2 Weak formulation of homogeneous Dirichlet problem Search u H0 1 (Ω) (here,

More information

Variations on Backpropagation

Variations on Backpropagation 2 Variations on Backpropagation 2 Variations Heuristic Modifications Moentu Variable Learning Rate Standard Nuerical Optiization Conjugate Gradient Newton s Method (Levenberg-Marquardt) 2 2 Perforance

More information

Determining the Robot-to-Robot Relative Pose Using Range-only Measurements

Determining the Robot-to-Robot Relative Pose Using Range-only Measurements Deterining the Robot-to-Robot Relative Pose Using Range-only Measureents Xun S Zhou and Stergios I Roueliotis Abstract In this paper we address the proble of deterining the relative pose of pairs robots

More information

Sharp Time Data Tradeoffs for Linear Inverse Problems

Sharp Time Data Tradeoffs for Linear Inverse Problems Sharp Tie Data Tradeoffs for Linear Inverse Probles Saet Oyak Benjain Recht Mahdi Soltanolkotabi January 016 Abstract In this paper we characterize sharp tie-data tradeoffs for optiization probles used

More information

Non-Parametric Non-Line-of-Sight Identification 1

Non-Parametric Non-Line-of-Sight Identification 1 Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,

More information

Uniform Approximation and Bernstein Polynomials with Coefficients in the Unit Interval

Uniform Approximation and Bernstein Polynomials with Coefficients in the Unit Interval Unifor Approxiation and Bernstein Polynoials with Coefficients in the Unit Interval Weiang Qian and Marc D. Riedel Electrical and Coputer Engineering, University of Minnesota 200 Union St. S.E. Minneapolis,

More information

Basic Concepts of Adaptive Finite Element Methods for Elliptic Boundary Value Problems

Basic Concepts of Adaptive Finite Element Methods for Elliptic Boundary Value Problems Basic Concepts of Adaptive Finite lement Methods for lliptic Boundary Value Problems Ronald H.W. Hoppe 1,2 1 Department of Mathematics, University of Houston 2 Institute of Mathematics, University of Augsburg

More information

Asynchronous Gossip Algorithms for Stochastic Optimization

Asynchronous Gossip Algorithms for Stochastic Optimization Asynchronous Gossip Algoriths for Stochastic Optiization S. Sundhar Ra ECE Dept. University of Illinois Urbana, IL 680 ssrini@illinois.edu A. Nedić IESE Dept. University of Illinois Urbana, IL 680 angelia@illinois.edu

More information

Solution of Non-Homogeneous Dirichlet Problems with FEM

Solution of Non-Homogeneous Dirichlet Problems with FEM Master Thesis Solution of Non-Homogeneous Dirichlet Problems with FEM Francesco Züger Institut für Mathematik Written under the supervision of Prof. Dr. Stefan Sauter and Dr. Alexander Veit August 27,

More information

1 Analysis of heat transfer in a single-phase transformer

1 Analysis of heat transfer in a single-phase transformer Assignent -7 Analysis of heat transr in a single-phase transforer The goal of the first assignent is to study the ipleentation of equivalent circuit ethod (ECM) and finite eleent ethod (FEM) for an electroagnetic

More information

1. Let a(x) > 0, and assume that u and u h are the solutions of the Dirichlet problem:

1. Let a(x) > 0, and assume that u and u h are the solutions of the Dirichlet problem: Mathematics Chalmers & GU TMA37/MMG800: Partial Differential Equations, 011 08 4; kl 8.30-13.30. Telephone: Ida Säfström: 0703-088304 Calculators, formula notes and other subject related material are not

More information

Variational Formulations

Variational Formulations Chapter 2 Variational Formulations In this chapter we will derive a variational (or weak) formulation of the elliptic boundary value problem (1.4). We will discuss all fundamental theoretical results that

More information

Stability Analysis of the Matrix-Free Linearly Implicit 2 Euler Method 3 UNCORRECTED PROOF

Stability Analysis of the Matrix-Free Linearly Implicit 2 Euler Method 3 UNCORRECTED PROOF 1 Stability Analysis of the Matrix-Free Linearly Iplicit 2 Euler Method 3 Adrian Sandu 1 andaikst-cyr 2 4 1 Coputational Science Laboratory, Departent of Coputer Science, Virginia 5 Polytechnic Institute,

More information

c 2010 Society for Industrial and Applied Mathematics

c 2010 Society for Industrial and Applied Mathematics SIAM J MATRIX ANAL APPL Vol 31, No 4, pp 1848 1872 c 21 Society for Industrial and Applied Matheatics STOCHASTIC GALERKIN MATRICES OLIVER G ERNST AND ELISABETH ULLMANN Abstract We investigate the structural,

More information

A BLOCK MONOTONE DOMAIN DECOMPOSITION ALGORITHM FOR A NONLINEAR SINGULARLY PERTURBED PARABOLIC PROBLEM

A BLOCK MONOTONE DOMAIN DECOMPOSITION ALGORITHM FOR A NONLINEAR SINGULARLY PERTURBED PARABOLIC PROBLEM INTERNATIONAL JOURNAL OF NUMERICAL ANALYSIS AND MODELING Volue 3, Nuber 2, Pages 211 231 c 2006 Institute for Scientific Coputing and Inforation A BLOCK MONOTONE DOMAIN DECOMPOSITION ALGORITHM FOR A NONLINEAR

More information

Optimal designs for estimating pairs of coefficients in Fourier regression models

Optimal designs for estimating pairs of coefficients in Fourier regression models Optial designs for estiating pairs of coefficients in Fourier regression odels Holger Dette Ruhr-Universität Bochu Faultät für Matheati 70 Bochu Gerany e-ail: holger.dette@rub.de Viatcheslav B. Melas St.

More information

STOPPING SIMULATED PATHS EARLY

STOPPING SIMULATED PATHS EARLY Proceedings of the 2 Winter Siulation Conference B.A.Peters,J.S.Sith,D.J.Medeiros,andM.W.Rohrer,eds. STOPPING SIMULATED PATHS EARLY Paul Glasseran Graduate School of Business Colubia University New Yor,

More information

Worst-case performance of critical path type algorithms

Worst-case performance of critical path type algorithms Intl. Trans. in Op. Res. 7 (2000) 383±399 www.elsevier.co/locate/ors Worst-case perforance of critical path type algoriths G. Singh, Y. Zinder* University of Technology, P.O. Box 123, Broadway, NSW 2007,

More information

The Fundamental Basis Theorem of Geometry from an algebraic point of view

The Fundamental Basis Theorem of Geometry from an algebraic point of view Journal of Physics: Conference Series PAPER OPEN ACCESS The Fundaental Basis Theore of Geoetry fro an algebraic point of view To cite this article: U Bekbaev 2017 J Phys: Conf Ser 819 012013 View the article

More information

Distributed Subgradient Methods for Multi-agent Optimization

Distributed Subgradient Methods for Multi-agent Optimization 1 Distributed Subgradient Methods for Multi-agent Optiization Angelia Nedić and Asuan Ozdaglar October 29, 2007 Abstract We study a distributed coputation odel for optiizing a su of convex objective functions

More information

Principles of Optimal Control Spring 2008

Principles of Optimal Control Spring 2008 MIT OpenCourseWare http://ocw.it.edu 16.323 Principles of Optial Control Spring 2008 For inforation about citing these aterials or our Ters of Use, visit: http://ocw.it.edu/ters. 16.323 Lecture 10 Singular

More information