HOMOGENEOUS LEAST SQUARES PROBLEM
|
|
- Georgiana Nash
- 5 years ago
- Views:
Transcription
1 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 RC he homogeneos Least qares Problem s frst defned dscssed. hen two methods for solvng the L-problem are presented. hese methods are based on the se of the generalzed egenvale decomposton the generalzed snglar vale decomposton, respectvely. nally, a nmercal example s gven.. INRODUCION he solton of a (non-homogeneos) lnear system x b s a typcal problem n photogrammetry. Usally, the system s, n addton, overdetermned (.e. x b ) the exstence of the solton s ensred by the Least qares condton. he solton of a homogeneos lnear system x 0 s a less common problem. However, some nterestng applcatons exst, where homogeneos systems natrally arse. rst, the eqaton ax + by + c 0 represents a straght lne n xy-plane. If the ponts ( x, y ),,..., n, le (exactly) on the lne, we have x x M x n y y M y n 0 a 0 b M M c 0.e. a homogeneos system x 0 wth a n 3-matrx. mlarly, makng the sbstttons nto the eqaton ax + by + cxy + dx + ey + f 0 of a conc secton (e.g. crcle or ellpse) leads also to a homogeneos system. In photogrammetry compter vson there are two mportant examples, where solton of a homogeneos system s needed (see e.g. (Mkhal et al., 00) (orsyth et al., 003)): he determnaton of the parameters of the Drect Lnear ransformaton (DL). he determnaton of the essental or fndamental matrx from the gven (measred) coordnates of the correspondng ponts n two mages. 34
2 the Photogrammetrc Jornal of nl, Vol. 9, No., 005. HE HOMOGENEOU L-PROLEM he solton of any homogeneos system x 0 has the followng specal featres: here s always the trval solton x 0, bt t s not nterestng. If x 0 s a solton, then kx, where k s a arbtrary scalar, s also a solton (only the ratos of the nkowns can be determned). n addtonal condton s ths reqred to fx the length of the solton vector (to exclde the trval solton). smple way s to fx some component of the solton vector, say x, to solve the rest of the nknowns x from the non-homogeneos system however, lead to dffcltes: If the (tre) vale of k 0 sch that kx 0. It s ths better to se a more general condton j x j j. hs may, x j happens to be zero, there exsts no scalar x where s a p -matrx (for smplcty, we choose x nstead of x α, becase the scalng s not crtcal). Note that the condton x x x contans the followng specal cases: If I, then x x x + x + L + x If [ I 0] p, then x x + x + L + x p If dag b, b, K, b ), then x b x + b x + L + b x ( We trn now to the second dffclty. It s obvos, that the homogeneos system x 0 has no solton (other than x 0), f n the rank of s fll,.e. rank ( ) (typcal case n practcal problems!). It s then natral to search for a solton that makes the contradcton small,.e. x 0. poplar condton s the Least qares crteron x mn ased on the precedng consderatons we state now the followng problem: Gven a n -matrx : n rank ( ) () fnd the L-solton xˆ, that flfls the condtons: ˆ x mn () xˆ (3) hs problem here s called the homogeneos least sqares problem. 35
3 the Photogrammetrc Jornal of nl, Vol. 9, No., HE OLUION O HE HOMOGENEOU L-PROLEM In ths chapter we present two solton methods for the homogeneos L-problem. he core of these methods s the comptaton of the generalzed egenvale decomposton or the generalzed snglar vale decomposton. hese decompostons are brefly prescrbed n the ppendx. Mathematcal detals can be fond e.g. n (Golb et al., 99). It s mportant from practcal pont of vew that many mathematcal software packages sch as ML contan these decompostons (Mathworks, 005). 3. olton sng the generalzed egenvale decomposton he solton that flfls the condtons method,.e. by mnmzng x mn x can be fond sng the Lagrange L( x, λ ) x + λ ( x ) (4) where λ s the Lagrange mltpler. ettng the partal dervatves of L wth respect to x λ to zero gves the eqatons x λ x (5) x (6) It can be seen mmedately that the frst condton (5) s satsfed by the generalzed egenvectors of the symmetrc matrces. Usng x λ x we frther have L x + λ ( x ) x x + λ ( x x) λ (7) hs, the solton that mnmzes L s, n partclar, the generalzed egenvector e mn related to the smallest generalzed egenvale λ mn,.e. x ˆ emn. In general, e α, therefore, n order to flfll the condton xˆ, a smple scalng s reqred: mn x ˆ (/ e e (8) mn ) mn 36
4 the Photogrammetrc Jornal of nl, Vol. 9, No., 005 In smmary, we have the followng solton algorthm:. Compte the matrces M N. Compte the generalzed egenvale decomposton ME NED of M N. 3. earch for the smallest generalzed egenvale λ mn choose the related egenvector e mn as the L-solton, that s x ˆ emn. 4. Make an approprate scalng, f necessary (e.g. xˆ scaled (/ emn ) emn ). here are two mportant specal cases, where t s sffcent to compte the (ordnary) egenvale decomposton nstead of the generalzed egenvale decomposton. rst, f I (fnd xˆ, sch that xˆ mn x ˆ ) the condton (5) redces to the more smple condton x λ x (9) he egenvectors of satsfy ths eqaton. t, sng x λ x x + λ ( x ) x x + λ ( x x) x ( x λx) + λ λ hs, the L-solton s the egenvector correspondng to the smallest egenvale of alternatve dervaton for ths reslt s gven n (orsyth et al., 003). he second nterestng specal case s [ 0 I ] p. n,.e. the constrant contans only a part of the parameters (the last p parameters here). y parttonng [ ] have the condtons: [ x x ] x x + x mn x (0) he L-solton can be fond n ths case by performng the followng steps: () Compte the coeffcent matrx of the redced normal eqaton ( x elmnated!) N ) ( ) ( ) () ( () Compte the egenvale decomposton of N. x we (3) Choose the egenvector correspondng to the smallest egenvale as the L-solton,.e. x ˆ e () mn (4) olve ˆx from the eqaton xˆ ˆ x (3) 37
5 the Photogrammetrc Jornal of nl, Vol. 9, No., olton sng the generalzed snglar vale decomposton Let s assme that matrces have the generalzed snglar vale decomposton U (4) V where U s an orthogonal nxn-matrx ( U U I), V s an orthogonal pxp-matrx ( V V I ), s a dagonal nx-matrx, s a dagonal px-matrx s a reglar (non-snglar) xmatrx (see the ppendx for more nformaton on the generalzed snglar vale decomposton). y notng that x x U x x V x x by defnng y x, the orgnal problem ( x mn sbject to x ) can be redced to the eqvalent problem y mn sbject to y (5) ssmng that the dagonal elements of are ordered as α + α the dagonal elements of are ordered as β + β, the solton of ths problem s smply the vector [ / β 0 0 ] yˆ K 0 (6) he L-solton xˆ can then be solved from the system xˆ yˆ (7) Note that the L-solton xˆ s the frst row of the nverse matrx Note also that the sqared resdal sm s smply α / β dvded by the scalar β. s (8).e. the sqare of the frst generalzed snglar vale of. We ths have the followng algorthm for the L-solton:. Compte the generalzed snglar vale decomposton of (, ). olve xˆ from the system xˆ yˆ, where yˆ [ / β 0 0 0] K. Compared to the frst algorthm, a favorable featre of ths algorthm s that the comptaton of matrces s avoded. 38
6 the Photogrammetrc Jornal of nl, Vol. 9, No., 005 In the specal case I the solton smplfes agan essentally. Let UV be the snglar vale decomposton of. hen x mn UV x mn frthermore UV the problem: x V x x V x. hs, by defnng y V x, the orgnal problem redces to nd ŷ sch that yˆ mn y ˆ. ecase s a dagonal matrx s > s +, the L-solton of ths problem s smply yˆ 0 0 K 0. hs the L-solton xˆ of the orgnal problem s [ ] 0 0 x ˆ Vyˆ V M V (9) 0.e. the last colmn V of the matrx V or the snglar vector related to the smallest snglar vale of. 4. N EXMPLE We demonstrate the homogeneos L-problem by fttng a straght lne to gven measred ponts x, y ) sng the model ( ax + by + c 0 or [ x y a ] b 0 c We consder the followng constrants for the parameters: b a + b + c a + b he frst constrant leads to snglar case, f the lne s parallel to y-axs. he second constrant has no partclar advantages apart from ts smplcty. hrd constrant on the contrary trns ot very nterestng (Hessan Normal orm). Note that the dstance of a pont ( x, y) from the lne ax + by + c 0 s ( ax + by + c) / a b. hs, f + b d + a, then d ax + by + c. herefore, when choosng ths constrant, the resdals are orthogonal to the ftted lne! 39
7 the Photogrammetrc Jornal of nl, Vol. 9, No., 005 y defnng [ a b c] x we fnd easly that the constrant a + b s eqvalent to x, where Gven the ponts {(,), (,6), (6,)} we also have 6 6 ˆ. fter he comptatons sng ML gve the L-solton x [ ] approprate scalng the ftted lne can be wrtten e.g. as y x + 6 ˆ ˆ. he resdal vector s v x [ ] When compared to other methods for lne fttng the followng featres of the presented methods are worth emphaszng: No snglar cases (orentaton of the lne can be arbtrary). No need for approxmate vales teratons. Orthogonal resdals are mnmzed. 5. REERENCE orsyth, D.., Ponce, J., 003. Compter Vson Modern pproach. Pearson Edcaton. Golb, G.H., van Loan, C.., 989. Matrx Comptatons, nd ed., he Johns Hopkns Unversty Press. Hartley, R., Zsserman,., 00. Mltple Vew Geometry, Cambrdge Unversty Press. Mkhal, E.M., ethel, J.., McGlone, J.C., 00. Introdcton to Modern Photogrammetry, John Wley & ons. Mathworks, 005. Docmentaton for Mathworks Prodcts, Release 4 wth ervce Pack, ( ). 40
8 the Photogrammetrc Jornal of nl, Vol. 9, No., 005 PPENDIX Egenvale decomposton Let M be a symmetrc x-matrx. hen there exst an orthogonal matrx E a dagonal matrx D dag( λ, K, λ ) sch that ME ED Dagonal element λ s the th egenvale of M the colmn e s the correspondng egenvector. Generalzed egenvale decomposton Let M N be symmetrc x-matrces. hen there exsts a reglar x-matrx G a dagonal matrx D dag( λ, K, λ ) sch that MG NGD Dagonal element λ s th generalzed egenvale of M N. he colmn correspondng egenvector. g s the nglar vale decomposton Let be a nx-matrx ( n > ). hen can be decomposed as UV or U V 0 or U V 0 where U s an orthogonal nxn-matrx, V s an orthogonal x-matrx s a dagonal nxmatrx. Generalzed snglar vale decomposton Let be a nx-matrs a px-matrx. hen can be decomposed as (Mathworks, 005) U V where U s an orthogonal nxn-matrx, V s an orthogonal pxp-matrx, s a xq-matrx ( q mn( n + p, ) ), s a dagonal nx-matrx (dagonal elements α + α ) s a dagonal px-matrx (dagonal elements β β + ). 4
9 the Photogrammetrc Jornal of nl, Vol. 9, No., 005 Matrces satsfy n ths formlaton the condton + he strctre of depends on n, p. or example, f n > p <, then, dag( α,..., α ), α + α 0 [ 0], dag( β,..., β p ), β + β n >, then s a sqare matrx (x). In addton, f the rank of [ ] Note that, f then s a reglar matrx s fll, where U V dag α / β,..., α / β ) ( Dagonal elements s are the generalzed snglar vales of. 4
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 informationCSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography
CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve
More informationSalmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2
Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to
More informationBAR & 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 informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More information1 Matrix representations of canonical matrices
1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:
More informationInner 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 informationρ some λ THE INVERSE POWER METHOD (or INVERSE ITERATION) , for , or (more usually) to
THE INVERSE POWER METHOD (or INVERSE ITERATION) -- applcaton of the Power method to A some fxed constant ρ (whch s called a shft), x λ ρ If the egenpars of A are { ( λ, x ) } ( ), or (more usually) to,
More informationEstimating the Fundamental Matrix by Transforming Image Points in Projective Space 1
Estmatng the Fundamental Matrx by Transformng Image Ponts n Projectve Space 1 Zhengyou Zhang and Charles Loop Mcrosoft Research, One Mcrosoft Way, Redmond, WA 98052, USA E-mal: fzhang,cloopg@mcrosoft.com
More informationSolutions to exam in SF1811 Optimization, Jan 14, 2015
Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable
More informationSE 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 informationLecture 3. Ax x i a i. i i
18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest
More informationMMA and GCMMA two methods for nonlinear optimization
MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons
More informationPerron Vectors of an Irreducible Nonnegative Interval Matrix
Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of
More informationComposite Hypotheses testing
Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter
More informationNorms, Condition Numbers, Eigenvalues and Eigenvectors
Norms, Condton Numbers, Egenvalues and Egenvectors 1 Norms A norm s a measure of the sze of a matrx or a vector For vectors the common norms are: N a 2 = ( x 2 1/2 the Eucldean Norm (1a b 1 = =1 N x (1b
More informationThe Prncpal Component Transform The Prncpal Component Transform s also called Karhunen-Loeve Transform (KLT, Hotellng Transform, oregenvector Transfor
Prncpal Component Transform Multvarate Random Sgnals A real tme sgnal x(t can be consdered as a random process and ts samples x m (m =0; ;N, 1 a random vector: The mean vector of X s X =[x0; ;x N,1] T
More informationSome modelling aspects for the Matlab implementation of MMA
Some modellng aspects for the Matlab mplementaton of MMA Krster Svanberg krlle@math.kth.se Optmzaton and Systems Theory Department of Mathematcs KTH, SE 10044 Stockholm September 2004 1. Consdered optmzaton
More informationYong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 )
Kangweon-Kyungk Math. Jour. 4 1996), No. 1, pp. 7 16 AN ITERATIVE ROW-ACTION METHOD FOR MULTICOMMODITY TRANSPORTATION PROBLEMS Yong Joon Ryang Abstract. The optmzaton problems wth quadratc constrants often
More informationLinear 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 informationReport 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 informationSTAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16
STAT 39: MATHEMATICAL COMPUTATIONS I FALL 218 LECTURE 16 1 why teratve methods f we have a lnear system Ax = b where A s very, very large but s ether sparse or structured (eg, banded, Toepltz, banded plus
More informationConvexity preserving interpolation by splines of arbitrary degree
Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete
More information1 GSW Iterative Techniques for y = Ax
1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn
More information= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system.
Chapter Matlab Exercses Chapter Matlab Exercses. Consder the lnear system of Example n Secton.. x x x y z y y z (a) Use the MATLAB command rref to solve the system. (b) Let A be the coeffcent matrx and
More informationAffine transformations and convexity
Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/
More informationChat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall, 1980
MT07: Multvarate Statstcal Methods Mke Tso: emal mke.tso@manchester.ac.uk Webpage for notes: http://www.maths.manchester.ac.uk/~mkt/new_teachng.htm. Introducton to multvarate data. Books Chat eld, C. and
More informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationSingular Value Decomposition: Theory and Applications
Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real
More informationThe Order Relation and Trace Inequalities for. Hermitian Operators
Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence
More informationInexact Newton Methods for Inverse Eigenvalue Problems
Inexact Newton Methods for Inverse Egenvalue Problems Zheng-jan Ba Abstract In ths paper, we survey some of the latest development n usng nexact Newton-lke methods for solvng nverse egenvalue problems.
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More information8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore
8/5/17 Data Modelng Patrce Koehl Department of Bologcal Scences atonal Unversty of Sngapore http://www.cs.ucdavs.edu/~koehl/teachng/bl59 koehl@cs.ucdavs.edu Data Modelng Ø Data Modelng: least squares Ø
More informationChapter 11: Simple Linear Regression and Correlation
Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests
More informationSL n (F ) Equals its Own Derived Group
Internatonal Journal of Algebra, Vol. 2, 2008, no. 12, 585-594 SL n (F ) Equals ts Own Derved Group Jorge Macel BMCC-The Cty Unversty of New York, CUNY 199 Chambers street, New York, NY 10007, USA macel@cms.nyu.edu
More informationCorrelation Clustering with Noisy Input
Correlaton Clsterng wth Nosy Inpt Clare Mathe Warren Schdy Brown Unversty SODA 2010 Nosy Correlaton Clsterng Model Unknown base clsterng B of n obects Nose: each edge s controlled by an adversary wth probablty
More informationAn Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors
An Algorthm to Solve the Inverse Knematcs Problem of a Robotc Manpulator Based on Rotaton Vectors Mohamad Z. Al-az*, Mazn Z. Othman**, and Baker B. Al-Bahr* *AL-Nahran Unversty, Computer Eng. Dep., Baghdad,
More informationFeb 14: Spatial analysis of data fields
Feb 4: Spatal analyss of data felds Mappng rregularly sampled data onto a regular grd Many analyss technques for geophyscal data requre the data be located at regular ntervals n space and/or tme. hs s
More informationSome Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)
Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998
More informationWhy Bayesian? 3. Bayes and Normal Models. State of nature: class. Decision rule. Rev. Thomas Bayes ( ) Bayes Theorem (yes, the famous one)
Why Bayesan? 3. Bayes and Normal Models Alex M. Martnez alex@ece.osu.edu Handouts Handoutsfor forece ECE874 874Sp Sp007 If all our research (n PR was to dsappear and you could only save one theory, whch
More informationAPPENDIX 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 informationIntegrals and Invariants of Euler-Lagrange Equations
Lecture 16 Integrals and Invarants of Euler-Lagrange Equatons ME 256 at the Indan Insttute of Scence, Bengaluru Varatonal Methods and Structural Optmzaton G. K. Ananthasuresh Professor, Mechancal Engneerng,
More informationLECTURE 9 CANONICAL CORRELATION ANALYSIS
LECURE 9 CANONICAL CORRELAION ANALYSIS Introducton he concept of canoncal correlaton arses when we want to quantfy the assocatons between two sets of varables. For example, suppose that the frst set of
More informationSolutions 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 informationU.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017
U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that
More informationGeneralized Linear Methods
Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set
More informationLinear Feature Engineering 11
Lnear Feature Engneerng 11 2 Least-Squares 2.1 Smple least-squares Consder the followng dataset. We have a bunch of nputs x and correspondng outputs y. The partcular values n ths dataset are x y 0.23 0.19
More informationOn the Multicriteria Integer Network Flow Problem
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of
More informationON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION
Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION
More information8.4 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 informationLecture 17: Lee-Sidford Barrier
CSE 599: Interplay between Convex Optmzaton and Geometry Wnter 2018 Lecturer: Yn Tat Lee Lecture 17: Lee-Sdford Barrer Dsclamer: Please tell me any mstake you notced. In ths lecture, we talk about the
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationCS 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 informationMaximal Margin Classifier
CS81B/Stat41B: Advanced Topcs n Learnng & Decson Makng Mamal Margn Classfer Lecturer: Mchael Jordan Scrbes: Jana van Greunen Corrected verson - /1/004 1 References/Recommended Readng 1.1 Webstes www.kernel-machnes.org
More informationStructure from Motion. Forsyth&Ponce: Chap. 12 and 13 Szeliski: Chap. 7
Structure from Moton Forsyth&once: Chap. 2 and 3 Szelsk: Chap. 7 Introducton to Structure from Moton Forsyth&once: Chap. 2 Szelsk: Chap. 7 Structure from Moton Intro he Reconstructon roblem p 3?? p p 2
More information6.854J / J Advanced Algorithms Fall 2008
MIT OpenCourseWare http://ocw.mt.edu 6.854J / 18.415J Advanced Algorthms Fall 2008 For nformaton about ctng these materals or our Terms of Use, vst: http://ocw.mt.edu/terms. 18.415/6.854 Advanced Algorthms
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationFormulas for the Determinant
page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use
More informationCSCE 790S Background Results
CSCE 790S Background Results Stephen A. Fenner September 8, 011 Abstract These results are background to the course CSCE 790S/CSCE 790B, Quantum Computaton and Informaton (Sprng 007 and Fall 011). Each
More informationTHERE ARE NO POINTS OF ORDER 11 ON ELLIPTIC CURVES OVER Q.
THERE ARE NO POINTS OF ORDER 11 ON ELLIPTIC CURVES OVER Q. IAN KIMING We shall prove the followng result from [2]: Theorem 1. (Bllng-Mahler, 1940, cf. [2]) An ellptc curve defned over Q does not have a
More informationHowever, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values
Fall 007 Soluton to Mdterm Examnaton STAT 7 Dr. Goel. [0 ponts] For the general lnear model = X + ε, wth uncorrelated errors havng mean zero and varance σ, suppose that the desgn matrx X s not necessarly
More informationMore metrics on cartesian products
More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationSupplement: Proofs and Technical Details for The Solution Path of the Generalized Lasso
Supplement: Proofs and Techncal Detals for The Soluton Path of the Generalzed Lasso Ryan J. Tbshran Jonathan Taylor In ths document we gve supplementary detals to the paper The Soluton Path of the Generalzed
More informationModule 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 informationQuantum Mechanics I - Session 4
Quantum Mechancs I - Sesson 4 Aprl 3, 05 Contents Operators Change of Bass 4 3 Egenvectors and Egenvalues 5 3. Denton....................................... 5 3. Rotaton n D....................................
More informationIntegrals and Invariants of
Lecture 16 Integrals and Invarants of Euler Lagrange Equatons NPTEL Course Varatonal Methods and Structural Optmzaton G. K. Ananthasuresh Professor, Mechancal Engneerng, Indan Insttute of Scence, Banagalore
More informationn α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0
MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector
More informationCS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras
CS4495/6495 Introducton to Computer Vson 3C-L3 Calbratng cameras Fnally (last tme): Camera parameters Projecton equaton the cumulatve effect of all parameters: M (3x4) f s x ' 1 0 0 0 c R 0 I T 3 3 3 x1
More informationOn the symmetric character of the thermal conductivity tensor
On the symmetrc character of the thermal conductvty tensor Al R. Hadjesfandar Department of Mechancal and Aerospace Engneerng Unversty at Buffalo, State Unversty of New York Buffalo, NY 146 USA ah@buffalo.edu
More informatione - c o m p a n i o n
OPERATIONS RESEARCH http://dxdoorg/0287/opre007ec e - c o m p a n o n ONLY AVAILABLE IN ELECTRONIC FORM 202 INFORMS Electronc Companon Generalzed Quantty Competton for Multple Products and Loss of Effcency
More informationChapter 7 Generalized and Weighted Least Squares Estimation. In this method, the deviation between the observed and expected values of
Chapter 7 Generalzed and Weghted Least Squares Estmaton The usual lnear regresson model assumes that all the random error components are dentcally and ndependently dstrbuted wth constant varance. When
More informationLecture 10 Support Vector Machines. Oct
Lecture 10 Support Vector Machnes Oct - 20-2008 Lnear Separators Whch of the lnear separators s optmal? Concept of Margn Recall that n Perceptron, we learned that the convergence rate of the Perceptron
More informationMATH 241B FUNCTIONAL ANALYSIS - NOTES EXAMPLES OF C ALGEBRAS
MATH 241B FUNCTIONAL ANALYSIS - NOTES EXAMPLES OF C ALGEBRAS These are nformal notes whch cover some of the materal whch s not n the course book. The man purpose s to gve a number of nontrval examples
More informationMEM 255 Introduction to Control Systems Review: Basics of Linear Algebra
MEM 255 Introducton to Control Systems Revew: Bascs of Lnear Algebra Harry G. Kwatny Department of Mechancal Engneerng & Mechancs Drexel Unversty Outlne Vectors Matrces MATLAB Advanced Topcs Vectors A
More informationCOMPLEX NUMBERS AND QUADRATIC EQUATIONS
COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More informationVector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence.
Vector Norms Chapter 7 Iteratve Technques n Matrx Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematcs Unversty of Calforna, Berkeley Math 128B Numercal Analyss Defnton A vector norm
More informationImportant Instructions to the Examiners:
Summer 0 Examnaton Subject & Code: asc Maths (70) Model Answer Page No: / Important Instructons to the Examners: ) The Answers should be examned by key words and not as word-to-word as gven n the model
More informationCanonical transformations
Canoncal transformatons November 23, 2014 Recall that we have defned a symplectc transformaton to be any lnear transformaton M A B leavng the symplectc form nvarant, Ω AB M A CM B DΩ CD Coordnate transformatons,
More informationLeast squares cubic splines without B-splines S.K. Lucas
Least squares cubc splnes wthout B-splnes S.K. Lucas School of Mathematcs and Statstcs, Unversty of South Australa, Mawson Lakes SA 595 e-mal: stephen.lucas@unsa.edu.au Submtted to the Gazette of the Australan
More informationLeast Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng
More information3/31/ = 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 informationThe Geometry of Logit and Probit
The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.
More informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationPhysics 5153 Classical Mechanics. Principle of Virtual Work-1
P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal
More informationHidden Markov Models & The Multivariate Gaussian (10/26/04)
CS281A/Stat241A: Statstcal Learnng Theory Hdden Markov Models & The Multvarate Gaussan (10/26/04) Lecturer: Mchael I. Jordan Scrbes: Jonathan W. Hu 1 Hdden Markov Models As a bref revew, hdden Markov models
More informationLecture 20: Lift and Project, SDP Duality. Today we will study the Lift and Project method. Then we will prove the SDP duality theorem.
prnceton u. sp 02 cos 598B: algorthms and complexty Lecture 20: Lft and Project, SDP Dualty Lecturer: Sanjeev Arora Scrbe:Yury Makarychev Today we wll study the Lft and Project method. Then we wll prove
More informationExact 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 informationC 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 informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationEQUATION 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 informationU.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016
U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and
More informationAn efficient algorithm for multivariate Maclaurin Newton transformation
Annales UMCS Informatca AI VIII, 2 2008) 5 14 DOI: 10.2478/v10065-008-0020-6 An effcent algorthm for multvarate Maclaurn Newton transformaton Joanna Kapusta Insttute of Mathematcs and Computer Scence,
More informationIntroduction to elastic wave equation. Salam Alnabulsi University of Calgary Department of Mathematics and Statistics October 15,2012
Introdcton to elastc wave eqaton Salam Alnabls Unversty of Calgary Department of Mathematcs and Statstcs October 15,01 Otlne Motvaton Elastc wave eqaton Eqaton of moton, Defntons and The lnear Stress-
More informationComputer Graphics. Curves and Surfaces. Hermite/Bezier Curves, (B-)Splines, and NURBS. By Ulf Assarsson
Compter Graphcs Crves and Srfaces Hermte/Bezer Crves, (B-)Splnes, and NURBS By Ulf Assarsson Most of the materal s orgnally made by Edward Angel and s adapted to ths corse by Ulf Assarsson. Some materal
More informationTransfer 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 informationNEWTON S LAWS. These laws only apply when viewed from an inertial coordinate system (unaccelerated system).
EWTO S LAWS Consder two partcles. 1 1. If 1 0 then 0 wth p 1 m1v. 1 1 2. 1.. 3. 11 These laws only apply when vewed from an nertal coordnate system (unaccelerated system). consder a collecton of partcles
More informationDynamic Systems on Graphs
Prepared by F.L. Lews Updated: Saturday, February 06, 200 Dynamc Systems on Graphs Control Graphs and Consensus A network s a set of nodes that collaborates to acheve what each cannot acheve alone. A network,
More informationCHALMERS, GÖTEBORGS UNIVERSITET. SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS. COURSE CODES: FFR 135, FIM 720 GU, PhD
CHALMERS, GÖTEBORGS UNIVERSITET SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS COURSE CODES: FFR 35, FIM 72 GU, PhD Tme: Place: Teachers: Allowed materal: Not allowed: January 2, 28, at 8 3 2 3 SB
More information