Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors

Size: px
Start display at page:

Download "Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors"

Transcription

1 Pont cloud to pont cloud rgd transformatons Russell Taylor Fall Copyrght R. H. Taylor Mnmzng Rgd Regstraton Errors Typcally, gven a set of ponts {a } n one coordnate system and another set of ponts {b } n a second coordnate system Goal s to fnd [R,p] that mnmzes η = where e e e = (R a + p) b Ths s trcky, because of R Fall Copyrght R. H. Taylor 1

2 Pont cloud to pont cloud regstraton a k b k R a k + p = b k Fall Copyrght R. H. Taylor Mnmzng Rgd Regstraton Errors Step 1: Compute N N 1 1 a = a b = b N = 1 N = 1 a" = a a b" = b b Step : Fnd R that mnmzes ( R a" b" ) Step 3: Fnd p p = b R a Step 4: Desred transformaton s F = Frame( R, p) Fall Copyrght R. H. Taylor

3 Pont cloud to pont cloud regstraton a a k b b k R a k + p = b k Fall Copyrght R. H. Taylor Pont cloud to pont cloud regstraton p = b Ra Fall Copyrght R. H. Taylor 3

4 Rotaton Estmaton Fall Copyrght R. H. Taylor Rotaton Estmaton? Fall Copyrght R. H. Taylor 4

5 Rotaton Estmaton Fall Copyrght R. H. Taylor Rotaton Estmaton Fall Copyrght R. H. Taylor 5

6 Pont cloud to pont cloud regstraton Fall Copyrght R. H. Taylor Solvng for R: teraton method Gven {,( "a, b " ),}, want to fnd R = arg mn R"a b " Step 0: Make an ntal guess R 0 Step 1: Gven R k, compute b = R 1" b k Step : Compute ΔR that mnmzes (ΔR "a b ) Step 3: Set R k +1 = R k ΔR Step 4: Iterate Steps 1-3 untl resdual error s suffcently small (or other termnaton condton) Fall Copyrght R. H. Taylor 6

7 Iteratve method: Gettng Intal Guess We want to fnd an approxmate soluton R 0 to R 0 "a b " One way to do ths s as follows. Form matrces A = "a B= b " Solve least-squares problem M 3x3 A 3xN B 3xN T T Note : You may fnd t easer to solve A 3xN M 3x3 Set R 0 = orthogonalze(m 3x3 ). Verfy that R s a rotaton T B 3xN Our problem s now to solve R 0 ΔRA B. I.e., ΔRA R 0 1 B Fall Copyrght R. H. Taylor Iteratve method: Solvng for ΔR Approxmate Δ R as ( I + skew( α)). I.e., Δ R v v + α v for any vector v. Then, our least squares problem becomes ( Δ R a b mn " ) mn ( a" b + α a" ) ΔR α Ths s lnear least squares problem n α. Then com pute ΔR( α) Fall Copyrght R. H. Taylor 7

8 Drect Iteratve approach for Rgd Frame Gven {,( a ", b " ),}, want to fnd F = argmn F a " b " Step 0: Make an ntal guess F 0 Step 1: Gven F k, compute " a k = F k " a Step : Compute ΔF that mnmzes ΔF " a k " b Step 3: Set F k+1 = ΔFF k Step 4: Iterate Steps 1-3 untl resdual error s suffcently small (or other termnaton condton) Fall Copyrght R. H. Taylor Drect Iteratve approach for Rgd Frame To solve for ΔF = argmn ΔF a k b ΔF a k b α a k + ε + a k b α a k + ε b a k sk( a k ) α + ε b a k Solve the least-squares problem " " sk( a k ) I " " α ε Now set ΔF=[ΔR( α), ε] " b a k " Fall Copyrght R. H. Taylor 8

9 Drect Technques to solve for R Method due to K. Arun, et. al., IEEE PAMI, Vol 9, no 5, pp , Sept 1987 Step 1: Compute H = a b,x,x a b,y,x a b,z,x a b,x,y a b,y,y a b,z,y a b,x,z a b,y,z a b,z,z Step : Compute the SVD of H = USV t Step 3: R = VU t Step 4: Verfy Det(R) = 1. If not, then algorthm may fal. Falure s rare, and mostly fxable. The paper has detals Fall Copyrght R. H. Taylor Quarternon Technque to solve for R B.K.P. Horn, Closed form soluton of absolute orentaton usng unt quaternons, J. Opt. Soc. Amerca, A vol. 4, no. 4, pp 69-64, Apr Method descrbed as reported n Besl and McKay, A method for regstraton of 3D shapes, IEEE Trans. on Pattern Analyss and Machne Intellgence, vol. 14, no., February 199. Solves a 4x4 egenvalue problem to fnd a unt quaternon correspondng to the rotaton Ths quaternon may be converted n closed form to get a more conventonal rotaton matrx Fall Copyrght R. H. Taylor 9

10 Dgresson: quaternons Invented by Hamlton n Can be thought of as 4 elements: q = q 0,q 1,q, scalar & vector: q = s + v = s, v Complex number: q = q 0 + q 1 + q j + k Propertes: where = j = k = j k = 1 Lnearty: λq 1 + µ q = λs 1 + µs,λ v 1 + µ v Conjugate: q * = s v = s, v Transform vector: Product: q 1 " q = s 1 s v 1 v,s v 1 + s v1 + v 1 v q " p = q " 0, p " q * Norm: q = s + v v = q 0 + q 1 + q Fall Copyrght R. H. Taylor Dgresson contnued: unt quaternons We can assocate a rotaton by angle θ about an axs n wth the unt quaternon: θ θ Rot( n, θ) cos, sn n Exercse: Demonstrate ths relatonshp. I.e., show θ θ θ θ Rot(( n, θ ) p = cos, sn n 0 "[, p ]" cos, sn n Fall Copyrght R. H. Taylor 10

11 A bt more on quaternons Exercse: show by substtuton that the varous formulatons for quaternons are equvalent A few web references: realnormedalgebra/quaternons/ndex.htm Fall Copyrght R. H. Taylor Rotaton matrx from unt quaternon q = q 0,q 1,q,q 3 ; q = 1 q 0 + q 1 q (q 1 q q 0 ) (q 1 + q 0 q ) R(q) = (q 1 q + q 0 ) q 0 q 1 + q (q q 0 q 1 ) (q 1 q 0 q ) (q + q 0 q 1 ) q 0 q 1 q + q Fall Copyrght R. H. Taylor 11

12 Unt quaternon from rotaton matrx R(q) = r xx r yx r zx r xy r yy r zy r xz r yz r zz ; a 0 = 1+ r xx + r yy + r zz ; a 1 = 1+ r xx r yy r zz a = 1 r xx + r yy r zz ; a 3 = 1 r xx r yy + r zz a 0 = max{a k } a 1 = max{a k } a = max{a k } a 3 = max{a k } q 0 = a 0 q 0 = r yz r zy 4q 1 q 0 = r zx r xz 4q q 0 = r xy r yx 4 q 1 = r r xy yx q 4q 1 = a 1 0 q 1 = r xy + r yx 4q q 1 = r xz + r zx 4 q = r zx r xz 4q 0 q = r + r xy yx q 4q = a 1 q = r yz + r zy 4 = r yz r zy 4q 0 = r xz + r zx 4q 1 = r + r yz zy q 4 = a Fall Copyrght R. H. Taylor Rotaton axs and angle from rotaton matrx Many optons, ncludng drect trgonemetrc soluton. But ths works: [ n,θ] ExtractAxsAngle(R) { } [s, v] ConvertToQuaternon(R) return([ v / v,atan(s / v ) Fall Copyrght R. H. Taylor 1

13 Step 1: Compute H = Quaternon method for R Step : Compute trace(h) G = Δ a b,x,x a b,,x y a b,z,x a b,x, y a b,, y y a b,z, y a b,x,z a b,,z y a b,z,z Δ T H + H T trace(h)i where Δ T = H,3 H 3, H 3,1 H 1,3 H 1, H,1 Step 3: Compute egen value decomposton of G dag(λ)=q T GQ Step 4: The egenvector Q k = q 0,q 1,q, correspondng to the largest egenvalue λ k s a unt quaternon correspondng to the rotaton Fall Copyrght R. H. Taylor Another Quaternon Method for R Let q = s + v be the unt quaternon correspondng to R. Let a and b be vectors wth b = R a then we have the quaternon equaton (s + v)(0 + a)(s v) = 0 + b (s + v)(0 + a) = (0 + b)(s + v) snce (s v)(s + v) = 1+ 0 Expandng the scalar and vector parts gves v a = v b s a + v a = s b + b v Rearrangng... ( b a) v = 0 s( b a) + ( b + a) v = Fall Copyrght R. H. Taylor 13

14 Another Quaternon Method for R Expressng ths as a matrx equaton b a 0 ( ) T ( b a ) sk ( b + a ) s v = If we now express the quaternon q as a 4-vector q = s, v T, we can express the rotaton problem as the constraned lnear system M( a, b) q = 0 4 q = Fall Copyrght R. H. Taylor Fall Copyrght R. H. Taylor Another Quaternon Method for R In general, we have many observatons, and we want to solve the problem n a least squares sense: where mn M q subject to q = 1 M = M( a 1, b 1 ) " M( a n, b n ) and n s the number of observatons Takng the sngular value decomposton of M=UΣV T ths to the easer problem mn UΣV T q X reduces ( ) = Σ y subject to y = V T q = q = 1 = U Σ y 14

15 Ths problem s just Another Quaternon Method for R σ mn Σ y 0 σ = σ σ 4 y subject to y = 1 where σ are the sngular values. Recall that SVD routnes typcally return the σ 0 and sorted n decreasng magntude. So σ 4 s the smallest sngular value and the value of y wth y = 1 that mnmzes Σ y s y= T 0,0,0,1. The correspondng value of q s gven by q = V y = V 4. Where V 4 s the 4th column of V Fall Copyrght R. H. Taylor Non-reflectve spatal smlarty (rgd+scale) a k b k σr a k + p = b k Fall Copyrght R. H. Taylor 15

16 Non-reflectve spatal smlarty Step 1: Compute a = 1 N a N b = 1 N =1 N b =1 "a = a a " b = b b Step : Estmate scale σ = "b "a Step 3: Fnd R that mnmzes (R σ"a b " ) ( ) Step 4: Fnd p p = b R a Step 5: Desred transformaton s F = SmlartyFrame(σ,R, p) Fall Copyrght R. H. Taylor 16

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching) Face Recognton Problem Face Verfcaton Problem Face Verfcaton (1:1 matchng) Querymage face query Face Recognton (1:N matchng) database Applcaton: Access Control www.vsage.com www.vsoncs.com Bometrc Authentcaton

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuity Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

More information

Rotation Matrices and Homogeneous Transformations

Rotation Matrices and Homogeneous Transformations Rotation Matrices and Homogeneous Transformations A coordinate frame in an n-dimensional space is defined by n mutually orthogonal unit vectors. In particular, for a two-dimensional (2D) space, i.e., n

More information

L10: Linear discriminants analysis

L10: Linear discriminants analysis L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2016. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

Regression Models for a Binary Response Using EXCEL and JMP

Regression Models for a Binary Response Using EXCEL and JMP SEMATECH 997 Statstcal Methods Symposum Austn Regresson Models for a Bnary Response Usng EXCEL and JMP Davd C. Trndade, Ph.D. STAT-TECH Consultng and Tranng n Appled Statstcs San Jose, CA Topcs Practcal

More information

On the Solution of Indefinite Systems Arising in Nonlinear Optimization

On the Solution of Indefinite Systems Arising in Nonlinear Optimization On the Soluton of Indefnte Systems Arsng n Nonlnear Optmzaton Slva Bonettn, Valera Ruggero and Federca Tnt Dpartmento d Matematca, Unverstà d Ferrara Abstract We consder the applcaton of the precondtoned

More information

Binomial Link Functions. Lori Murray, Phil Munz

Binomial Link Functions. Lori Murray, Phil Munz Bnomal Lnk Functons Lor Murray, Phl Munz Bnomal Lnk Functons Logt Lnk functon: ( p) p ln 1 p Probt Lnk functon: ( p) 1 ( p) Complentary Log Log functon: ( p) ln( ln(1 p)) Motvatng Example A researcher

More information

GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM

GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 -NORM BARRIOT Jean-Perre, SARRAILH Mchel BGI/CNES 18.av.E.Beln 31401 TOULOUSE Cedex 4 (France) Emal: jean-perre.barrot@cnes.fr 1/Introducton The

More information

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful

More information

Faraday's Law of Induction

Faraday's Law of Induction Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Lecture 5,6 Linear Methods for Classification. Summary

Lecture 5,6 Linear Methods for Classification. Summary Lecture 5,6 Lnear Methods for Classfcaton Rce ELEC 697 Farnaz Koushanfar Fall 2006 Summary Bayes Classfers Lnear Classfers Lnear regresson of an ndcator matrx Lnear dscrmnant analyss (LDA) Logstc regresson

More information

where the coordinates are related to those in the old frame as follows.

where the coordinates are related to those in the old frame as follows. Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product

More information

Adding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors

Adding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors 1 Chapter 13. VECTORS IN THREE DIMENSIONAL SPACE Let s begin with some names and notation for things: R is the set (collection) of real numbers. We write x R to mean that x is a real number. A real number

More information

PERRON FROBENIUS THEOREM

PERRON FROBENIUS THEOREM PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()

More information

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem. Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT Chapter 4 ECOOMIC DISATCH AD UIT COMMITMET ITRODUCTIO A power system has several power plants. Each power plant has several generatng unts. At any pont of tme, the total load n the system s met by the

More information

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model

More information

A frequency decomposition time domain model of broadband frequency-dependent absorption: Model II

A frequency decomposition time domain model of broadband frequency-dependent absorption: Model II A frequenc decomposton tme doman model of broadband frequenc-dependent absorpton: Model II W. Chen Smula Research Laborator, P. O. Box. 134, 135 Lsaker, Norwa (1 Aprl ) (Proect collaborators: A. Bounam,

More information

Software Alignment for Tracking Detectors

Software Alignment for Tracking Detectors Software Algnment for Trackng Detectors V. Blobel Insttut für Expermentalphysk, Unverstät Hamburg, Germany Abstract Trackng detectors n hgh energy physcs experments requre an accurate determnaton of a

More information

Stochastic Six-Degree-of-Freedom Flight Simulator for Passively Controlled High-Power Rockets

Stochastic Six-Degree-of-Freedom Flight Simulator for Passively Controlled High-Power Rockets Stochastc Sx-Degree-of-Freedom Flght for Passvely Controlled Hgh-Power s Smon Box 1 ; Chrstopher M. Bshop 2 ; and Hugh Hunt 3 Downloaded from ascelbrary.org by TECHNISCHE UNIVERSITEIT DELFT on 2/7/13.

More information

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!

More information

The Effect of Mean Stress on Damage Predictions for Spectral Loading of Fiberglass Composite Coupons 1

The Effect of Mean Stress on Damage Predictions for Spectral Loading of Fiberglass Composite Coupons 1 EWEA, Specal Topc Conference 24: The Scence of Makng Torque from the Wnd, Delft, Aprl 9-2, 24, pp. 546-555. The Effect of Mean Stress on Damage Predctons for Spectral Loadng of Fberglass Composte Coupons

More information

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.

More information

Loop Parallelization

Loop Parallelization - - Loop Parallelzaton C-52 Complaton steps: nested loops operatng on arrays, sequentell executon of teraton space DECLARE B[..,..+] FOR I :=.. FOR J :=.. I B[I,J] := B[I-,J]+B[I-,J-] ED FOR ED FOR analyze

More information

How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole

How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole CHAPTER 7 VECTOR BUNDLES We next begn addressng the queston: how do we assemble the tangent spaces at varous ponts of a manfold nto a coherent whole? In order to gude the decson, consder the case of U

More information

How To Calculate The Accountng Perod Of Nequalty

How To Calculate The Accountng Perod Of Nequalty Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton

More information

A Three-Point Combined Compact Difference Scheme

A Three-Point Combined Compact Difference Scheme JOURNAL OF COMPUTATIONAL PHYSICS 140, 370 399 (1998) ARTICLE NO. CP985899 A Three-Pont Combned Compact Derence Scheme Peter C. Chu and Chenwu Fan Department o Oceanography, Naval Postgraduate School, Monterey,

More information

GENERALIZED PROCRUSTES ANALYSIS AND ITS APPLICATIONS IN PHOTOGRAMMETRY

GENERALIZED PROCRUSTES ANALYSIS AND ITS APPLICATIONS IN PHOTOGRAMMETRY SWISS FEDERL INSIUE OF ECHNOLOGY Insttute of Geodes and Photograetr EH-Hoenggerberg, Zuerch GENERLIZED PROCRUSES NLYSIS ND IS PPLICIONS IN PHOOGRMMERY Prepared for: Praktku n Photograetre, Fernerkundung

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

In the rth step, the computaton of the Householder matrx H r requres only the n ; r last elements of the rth column of A T r;1a r;1 snce we donothave

In the rth step, the computaton of the Householder matrx H r requres only the n ; r last elements of the rth column of A T r;1a r;1 snce we donothave An accurate bdagonal reducton for the computaton of sngular values Ru M. S. Ralha Abstract We present a new bdagonalzaton technque whch s compettve wth the standard bdagonalzaton method and analyse the

More information

Matching Images with Different Resolutions

Matching Images with Different Resolutions Matchng Images wth Dfferent Resolutons Yves Dufournaud, Cordela Schmd, Radu Horaud To cte ths verson: Yves Dufournaud, Cordela Schmd, Radu Horaud. Matchng Images wth Dfferent Resolutons. Internatonal Conference

More information

21 Vectors: The Cross Product & Torque

21 Vectors: The Cross Product & Torque 21 Vectors: The Cross Product & Torque Do not use our left hand when applng ether the rght-hand rule for the cross product of two vectors dscussed n ths chapter or the rght-hand rule for somethng curl

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 14 MORE ABOUT REGRESSION CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp

More information

An Integrated Semantically Correct 2.5D Object Oriented TIN. Andreas Koch

An Integrated Semantically Correct 2.5D Object Oriented TIN. Andreas Koch An Integrated Semantcally Correct 2.5D Object Orented TIN Andreas Koch Unverstät Hannover Insttut für Photogrammetre und GeoInformaton Contents Introducton Integraton of a DTM and 2D GIS data Semantcs

More information

O(n) mass matrix inversion for serial manipulators and polypeptide chains using Lie derivatives Kiju Lee, Yunfeng Wang and Gregory S.

O(n) mass matrix inversion for serial manipulators and polypeptide chains using Lie derivatives Kiju Lee, Yunfeng Wang and Gregory S. Robotca 7) volume 5, pp 739 75 7 Cambrdge Unversty Press do:7/s6357477385 Prnted n the Unted Kngdom On) mass matrx nverson for seral manpulators and polypeptde chans usng Le dervatves Ku Lee, Yunfeng Wang

More information

Consider a 1-D stationary state diffusion-type equation, which we will call the generalized diffusion equation from now on:

Consider a 1-D stationary state diffusion-type equation, which we will call the generalized diffusion equation from now on: Chapter 1 Boundary value problems Numercal lnear algebra technques can be used for many physcal problems. In ths chapter we wll gve some examples of how these technques can be used to solve certan boundary

More information

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson

More information

Logistic Regression. Steve Kroon

Logistic Regression. Steve Kroon Logstc Regresson Steve Kroon Course notes sectons: 24.3-24.4 Dsclamer: these notes do not explctly ndcate whether values are vectors or scalars, but expects the reader to dscern ths from the context. Scenaro

More information

Goals Rotational quantities as vectors. Math: Cross Product. Angular momentum

Goals Rotational quantities as vectors. Math: Cross Product. Angular momentum Physcs 106 Week 5 Torque and Angular Momentum as Vectors SJ 7thEd.: Chap 11.2 to 3 Rotatonal quanttes as vectors Cross product Torque expressed as a vector Angular momentum defned Angular momentum as a

More information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Fragility Based Rehabilitation Decision Analysis

Fragility Based Rehabilitation Decision Analysis .171. Fraglty Based Rehabltaton Decson Analyss Cagdas Kafal Graduate Student, School of Cvl and Envronmental Engneerng, Cornell Unversty Research Supervsor: rcea Grgoru, Professor Summary A method s presented

More information

PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB.

PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. INDEX 1. Load data usng the Edtor wndow and m-fle 2. Learnng to save results from the Edtor wndow. 3. Computng the Sharpe Rato 4. Obtanng the Treynor Rato

More information

Numerical Methods 數 值 方 法 概 說. Daniel Lee. Nov. 1, 2006

Numerical Methods 數 值 方 法 概 說. Daniel Lee. Nov. 1, 2006 Numercal Methods 數 值 方 法 概 說 Danel Lee Nov. 1, 2006 Outlnes Lnear system : drect, teratve Nonlnear system : Newton-lke Interpolatons : polys, splnes, trg polys Approxmatons (I) : orthogonal polys Approxmatons

More information

The Distribution of Eigenvalues of Covariance Matrices of Residuals in Analysis of Variance

The Distribution of Eigenvalues of Covariance Matrices of Residuals in Analysis of Variance JOURNAL OF RESEARCH of the Natonal Bureau of Standards - B. Mathem atca l Scence s Vol. 74B, No.3, July-September 1970 The Dstrbuton of Egenvalues of Covarance Matrces of Resduals n Analyss of Varance

More information

1.5. Factorisation. Introduction. Prerequisites. Learning Outcomes. Learning Style

1.5. Factorisation. Introduction. Prerequisites. Learning Outcomes. Learning Style Factorisation 1.5 Introduction In Block 4 we showed the way in which brackets were removed from algebraic expressions. Factorisation, which can be considered as the reverse of this process, is dealt with

More information

IMPROVEMENT OF THE STABILITY SOLVING RATIONAL POLYNOMIAL COEFFICIENTS

IMPROVEMENT OF THE STABILITY SOLVING RATIONAL POLYNOMIAL COEFFICIENTS IMPROVEMEN OF HE ABIIY OVING RAIONA POYNOMIA COEFFICIEN Xanyong n a,*, Xuxao Yuan a a chool of Remote ensng and Informaton Engneerng, Wuhan Unversty, 19 uoyu Road, Wuhan 40079, Chna wnterlnny@16.com Commsson

More information

Chapter 7 Symmetry and Spectroscopy Molecular Vibrations p. 1 -

Chapter 7 Symmetry and Spectroscopy Molecular Vibrations p. 1 - Chapter 7 Symmetry and Spectroscopy Molecular Vbratons p - 7 Symmetry and Spectroscopy Molecular Vbratons 7 Bases for molecular vbratons We nvestgate a molecule consstng of N atoms, whch has 3N degrees

More information

How To Solve A Problem In A Powerline (Powerline) With A Powerbook (Powerbook)

How To Solve A Problem In A Powerline (Powerline) With A Powerbook (Powerbook) MIT 8.996: Topc n TCS: Internet Research Problems Sprng 2002 Lecture 7 March 20, 2002 Lecturer: Bran Dean Global Load Balancng Scrbe: John Kogel, Ben Leong In today s lecture, we dscuss global load balancng

More information

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2) MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total

More information

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set

More information

Boundary Element Domain Decomposition Methods Challenges and Applications

Boundary Element Domain Decomposition Methods Challenges and Applications Boundary Element Doman Decomposton Methods Challenges and Applcatons Olaf Stenbach Insttut für Numersche Mathematk Technsche Unverstät Graz SFB 404 Mehrfeldprobleme n der Kontnuumsmechank, Stuttgart n

More information

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background: SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and

More information

Energies of Network Nastsemble

Energies of Network Nastsemble Supplementary materal: Assessng the relevance of node features for network structure Gnestra Bancon, 1 Paolo Pn,, 3 and Matteo Marsl 1 1 The Abdus Salam Internatonal Center for Theoretcal Physcs, Strada

More information

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Period and Deadline Selection for Schedulability in Real-Time Systems

Period and Deadline Selection for Schedulability in Real-Time Systems Perod and Deadlne Selecton for Schedulablty n Real-Tme Systems Thdapat Chantem, Xaofeng Wang, M.D. Lemmon, and X. Sharon Hu Department of Computer Scence and Engneerng, Department of Electrcal Engneerng

More information

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008 Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn

More information

+ + + - - This circuit than can be reduced to a planar circuit

+ + + - - This circuit than can be reduced to a planar circuit MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to

More information

Formulating & Solving Integer Problems Chapter 11 289

Formulating & Solving Integer Problems Chapter 11 289 Formulatng & Solvng Integer Problems Chapter 11 289 The Optonal Stop TSP If we drop the requrement that every stop must be vsted, we then get the optonal stop TSP. Ths mght correspond to a ob sequencng

More information

Portfolio Loss Distribution

Portfolio Loss Distribution Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment

More information

Sect 6.1 - Greatest Common Factor and Factoring by Grouping

Sect 6.1 - Greatest Common Factor and Factoring by Grouping Sect 6.1 - Greatest Common Factor and Factoring by Grouping Our goal in this chapter is to solve non-linear equations by breaking them down into a series of linear equations that we can solve. To do this,

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

A Fast Incremental Spectral Clustering for Large Data Sets

A Fast Incremental Spectral Clustering for Large Data Sets 2011 12th Internatonal Conference on Parallel and Dstrbuted Computng, Applcatons and Technologes A Fast Incremental Spectral Clusterng for Large Data Sets Tengteng Kong 1,YeTan 1, Hong Shen 1,2 1 School

More information

Modern Problem Solving Techniques in Engineering with POLYMATH, Excel and MATLAB. Introduction

Modern Problem Solving Techniques in Engineering with POLYMATH, Excel and MATLAB. Introduction Modern Problem Solvng Tehnques n Engneerng wth POLYMATH, Exel and MATLAB. Introduton Engneers are fundamentally problem solvers, seekng to aheve some objetve or desgn among tehnal, soal eonom, regulatory

More information

A New Technique for Vehicle Tracking on the Assumption of Stratospheric Platforms. Department of Civil Engineering, University of Tokyo **

A New Technique for Vehicle Tracking on the Assumption of Stratospheric Platforms. Department of Civil Engineering, University of Tokyo ** Fuse, Taash A New Technque for Vehcle Tracng on the Assumton of Stratosherc Platforms Taash FUSE * and Ehan SHIMIZU ** * Deartment of Cvl Engneerng, Unversty of Toyo ** Professor, Deartment of Cvl Engneerng,

More information

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process Dsadvantages of cyclc TDDB47 Real Tme Systems Manual scheduler constructon Cannot deal wth any runtme changes What happens f we add a task to the set? Real-Tme Systems Laboratory Department of Computer

More information

Attitude and Position Estimation on the Mars Exploration Rovers

Attitude and Position Estimation on the Mars Exploration Rovers Atttude and Poston Estmaton on the Mars Exploraton Rovers Khaled S. Al, C. Anthony Vanell, Jeffrey J. esadeck, Mark W. Mamone, Yang Cheng, A. Mguel San Martn, and James W. Alexander Jet Propulson Laboratory

More information

General Iteration Algorithm for Classification Ratemaking

General Iteration Algorithm for Classification Ratemaking General Iteraton Algorthm for Classfcaton Ratemakng by Luyang Fu and Cheng-sheng eter Wu ABSTRACT In ths study, we propose a flexble and comprehensve teraton algorthm called general teraton algorthm (GIA)

More information

Extending Probabilistic Dynamic Epistemic Logic

Extending Probabilistic Dynamic Epistemic Logic Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set

More information

CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES

CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable

More information

Learning from Multiple Outlooks

Learning from Multiple Outlooks Learnng from Multple Outlooks Maayan Harel Department of Electrcal Engneerng, Technon, Hafa, Israel She Mannor Department of Electrcal Engneerng, Technon, Hafa, Israel maayanga@tx.technon.ac.l she@ee.technon.ac.l

More information

5 Solving systems of non-linear equations

5 Solving systems of non-linear equations umercal Methods n Chemcal Engneerng 5 Solvng systems o non-lnear equatons 5 Solvng systems o non-lnear equatons... 5. Overvew... 5. assng unctons... 5. D ewtons Method somethng you dd at school... 5. ewton's

More information

Inter-Ing 2007. INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 15-16 November 2007.

Inter-Ing 2007. INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 15-16 November 2007. Inter-Ing 2007 INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 15-16 November 2007. UNCERTAINTY REGION SIMULATION FOR A SERIAL ROBOT STRUCTURE MARIUS SEBASTIAN

More information

Lecture Topics. 6. Sensors and instrumentation 7. Actuators and power transmission devices. (System and Signal Processing) DR.1 11.12.

Lecture Topics. 6. Sensors and instrumentation 7. Actuators and power transmission devices. (System and Signal Processing) DR.1 11.12. Lecture Tocs 1. Introducton 2. Basc knematcs 3. Pose measurement and Measurement of Robot Accuracy 4. Trajectory lannng and control 5. Forces, moments and Euler s laws 5. Fundamentals n electroncs and

More information

Quantization Effects in Digital Filters

Quantization Effects in Digital Filters Quantzaton Effects n Dgtal Flters Dstrbuton of Truncaton Errors In two's complement representaton an exact number would have nfntely many bts (n general). When we lmt the number of bts to some fnte value

More information

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers Foundatons and Trends R n Machne Learnng Vol. 3, No. 1 (2010) 1 122 c 2011 S. Boyd, N. Parkh, E. Chu, B. Peleato and J. Ecksten DOI: 10.1561/2200000016 Dstrbuted Optmzaton and Statstcal Learnng va the

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

COMPRESSED NETWORK MONITORING. Mark Coates, Yvan Pointurier and Michael Rabbat

COMPRESSED NETWORK MONITORING. Mark Coates, Yvan Pointurier and Michael Rabbat COMPRESSED NETWORK MONITORING Mark Coates, Yvan Ponturer and Mchael Rabbat Department of Electrcal and Computer Engneerng McGll Unversty 348 Unversty Street, Montreal, Quebec H3A 2A7, Canada ABSTRACT Ths

More information

Homework: 49, 56, 67, 60, 64, 74 (p. 234-237)

Homework: 49, 56, 67, 60, 64, 74 (p. 234-237) Hoework: 49, 56, 67, 60, 64, 74 (p. 34-37) 49. bullet o ass 0g strkes a ballstc pendulu o ass kg. The center o ass o the pendulu rses a ertcal dstance o c. ssung that the bullet reans ebedded n the pendulu,

More information

Vehicle Detection, Classification and Position Estimation based on Monocular Video Data during Night-time

Vehicle Detection, Classification and Position Estimation based on Monocular Video Data during Night-time Vehcle Detecton, Classfcaton and Poston Estmaton based on Monocular Vdeo Data durng Nght-tme Jonas Frl, Marko H. Hoerter, Martn Lauer and Chrstoph Stller Keywords: Automotve Lghtng, Lght-based Drver Assstance,

More information

Damage detection in composite laminates using coin-tap method

Damage detection in composite laminates using coin-tap method Damage detecton n composte lamnates usng con-tap method S.J. Km Korea Aerospace Research Insttute, 45 Eoeun-Dong, Youseong-Gu, 35-333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The con-tap test has the

More information

A high-order compact method for nonlinear Black-Scholes option pricing equations of American Options

A high-order compact method for nonlinear Black-Scholes option pricing equations of American Options Bergsche Unverstät Wuppertal Fachberech Mathematk und Naturwssenschaften Lehrstuhl für Angewandte Mathematk und Numersche Mathematk Lehrstuhl für Optmerung und Approxmaton Preprnt BUW-AMNA-OPAP 10/13 II

More information

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble

More information

Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering

Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering Out-of-Sample Extensons for LLE, Isomap, MDS, Egenmaps, and Spectral Clusterng Yoshua Bengo, Jean-Franços Paement, Pascal Vncent Olver Delalleau, Ncolas Le Roux and Mare Oumet Département d Informatque

More information

A Multi-mode Image Tracking System Based on Distributed Fusion

A Multi-mode Image Tracking System Based on Distributed Fusion A Mult-mode Image Tracng System Based on Dstrbuted Fuson Ln zheng Chongzhao Han Dongguang Zuo Hongsen Yan School of Electroncs & nformaton engneerng, X an Jaotong Unversty X an, Shaanx, Chna Lnzheng@malst.xjtu.edu.cn

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby s Alg. for Maximal Independent Sets using Pairwise Independence Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

More information

Dynamic Load Balancing of Parallel Computational Iterative Routines on Platforms with Memory Heterogeneity

Dynamic Load Balancing of Parallel Computational Iterative Routines on Platforms with Memory Heterogeneity Dynamc Load Balancng of Parallel Comutatonal Iteratve Routnes on Platforms wth Memory Heterogenety Davd Clare, Alexey Lastovetsy, Vladmr Rychov School of Comuter Scence and Informatcs, Unversty College

More information

FACTORING ax 2 bx c. Factoring Trinomials with Leading Coefficient 1

FACTORING ax 2 bx c. Factoring Trinomials with Leading Coefficient 1 5.7 Factoring ax 2 bx c (5-49) 305 5.7 FACTORING ax 2 bx c In this section In Section 5.5 you learned to factor certain special polynomials. In this section you will learn to factor general quadratic polynomials.

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

Linear and quadratic Taylor polynomials for functions of several variables.

Linear and quadratic Taylor polynomials for functions of several variables. ams/econ 11b supplementary notes ucsc Linear quadratic Taylor polynomials for functions of several variables. c 010, Yonatan Katznelson Finding the extreme (minimum or maximum) values of a function, is

More information

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The

More information