Algorithm for reduction of Element Calculus to Element Algebra

Size: px
Start display at page:

Download "Algorithm for reduction of Element Calculus to Element Algebra"

Transcription

1 Algorth for reducton of Eleent Calculus to Eleent Algebra Introducton M. Manukyan, V. Harutunyan The XML databases currently act defnte nterest aong researchers of databases for the followng reasons: 1. DTD s a coprose between the strct-schea odels such as the relatonal or object odels and the copletely schealess world of se-structured data, 2. In contrast to se-structured data odel, the concept of database schea n the sense of conventonal data odels s supported, 3. In contrast to conventonal data odels strct-scheas, there s possblty to defne ore flexble database scheas (DTD s often allow optonal felds or ssng felds, for nstance) [4]. It s coon n database theory to translate a query language nto an algebra. The XML algebra s used to: 1. Defne seantcs of a query language, 2. Analyze power of ts expressons, 3. Perfor optzaton of queres. There are any approaches n lterature to solve the query proble over XML DB [for exaple, 1,3,5,8,9]. In ths paper we consder an algorth for reducton of eleent calculus [6] to eleent algebra [7]. Prelnares Eleent Calculus: Eleent calculus forulas (sply forulas) are used to gve assertons (e.g., consstency constrants of xd-database to support vrtual eleents and trggers, to express declaratve queres to xd-database. To specfy forulas a varant of the ultsorted frst order predcate logc language s used. Notce that eleent calculus s developed n the style of object calculus. If e s a path expresson (e) s the result of ths path expresson converted to ultset. The atos are defned accordng to the followng rules: ( e )( x1, x2,..., x k ) s an ato, where k ] x s eleent calculus varable. y1θ y2, where y 1, y 2 are ters, θ { =,, >,, <, }. Assung that ψ, ψ 1 and ψ 2 are forulas, the followng expressons wll be forulas: ψ, ψ 1 ψ 2, ψ 1 ψ 2, ψ 1 ψ 2, x( ψ x( ψ ( ψ ) x : = e, ( groupby( x1, x2,..., xk, ψ ( x), dstnct( ψ ) orderby( x ord x ord,..., xk ord, ψ ψ wth dentfcatonlst 1, 2 Algebrac Model: To drectly apply the standard algebrac operatons to xd-eleents we need: foralze the xd-eleent n coplance wth the theory of relatonal databases, defne the nference rules of the resultng scheas of algebrac expressons, prove equvalence of eleent calculus to eleent algebra.

2 Defnton 1: We say that S s an xd-eleent schea, f S = nae, atoctype, f, where f {?,*, +, }, or S = nae typeop( S, S,..., S n ), f, typeop { sequence, choce, all}, S s an xd-eleent schea, 1 n. Defnton 2: The xd-eleent s of schea S s a fnte collecton of S doan frstcop S doan sec ondcop S ; appngs ( ( ( ( f ondcop ( S ) typeop( S, S,..., ) and sec = S n the followng constrant should be hold for all e s : e[ S ] doan( S 1 n. The frstcop, sec ondcop, doan functons have an obvous seantcs n the prevous defnton. Notce that doan ( sec ondcop( S = ( f sec ondcop( S ) = atoctype valset n = 1 ( atoctype) else doan ( ) S. Defnton 3: Let R and Q be xd-eleents scheas. We say that R and Q are slar, f sec ondcop ( R) atoctype1,sec ondcop( Q) = atoctype2, = and atoctype 1 = atoctype2, or ondcop ( R) typeop( R, R,..., R sec ondcop( Q) = typeop( Q, Q,..., Q sec = n n and R, Q are slar, 1 n. att book2 book1 slar sequence att att sequence app OneorMor ttle type strng author type strng ttle2 type strng author type strng Defnton 4: Let R and Q be xd-eleents scheas. We say that R s subschea of Q ( R Q), f frstcop ( R) = nae1,sec ondcop( R) = atoctype1, frstcop( Q) = nae 2, sec ondcop ( Q) = atoctype 2, and nae 1 = nae 2, atoctype1 = atoctype 2, or frstcop( R) = nae1,sec ondcop( R) = typeop( R1, R2,..., Rk frstcop( Q) = nae 2, ondcop ( Q) = typeop( Q, Q,..., Q and nae 1 = nae 2, and k] j ] sec 2 that R Q. j 1

3 book subschea book type app sequence app sequen att OneorMore ttle type strng ttle type strng author type strng author type strng Defnton 5: Let r and q be xd-eleents wth R and Q slar scheas correspondngly. Let us say that r and q are equal, f sec ondcop ( R) sec ondcop( Q) = atoctype, content ( r) = content( q or sec ondcop ( R) = typeop( R1, R2,..., Rn sec ondcop ( Q) = typeop( Q1, Q2,..., Qn ): a) typeop = sequance n] frstcop( R ) = frstcop( ) = and, Q, and xd-eleents wth slar scheas R and Q correspondngly, b) typeop = all, n] j n] frstcop( R ) = frstcop( Q j and equal xd-eleents wth slar scheas R and c) typeop = choce, there s a unque [ 1, n] unque j [ 1, n] R frstcop frstcop = Q j, and : ( ) ( ) wth slar scheas Algorth Descrpton R and Q j correspondngly. Algorth: ECToEA (ec, ea) Input: ec - a forula of eleent calculus. Output: ea - an equvalent expresson of eleent algebra for ec. Method: Step1. Create prefx representaton for ec. Step2. Create Eleent Calculus tree. Step3. Create Eleent Algebra tree. Q j correspondngly, r and q are equal r and q j are such that the followng holds for soe r and Create the prefx representaton for an expresson of Eleent Calculus. Eleent Calculus tree creaton. Algorth: CreateECTree (eprefx, ECTree) Input: eprefx - a prefx expresson of eleent calculus forula. q j are equal xd-eleents

4 Output: ECTree - an equvalent eleent calculus tree. Step1. [Tree creaton] (a) If current token s and, or, plcaton, create correspondng node based on recursvely created left and rght subtrees. (b) If current token s ether wth, forall, exst, negaton, dstnct, groupby or orderby, create correspondng node and recursvely create ts subtree (operand (s) of operaton). (c) If current token s predcate, assgnent, xpath, create correspondng node and coplete recurson for ths subtree (operand (s) of operaton). Step2. [Tree traversal] (a) For each assgent node ove to the root untl a subtree whch set of free varables contans the varables set of consdered assgnent wll be found. (b) For each predcate node ove to the root untl a subtree whch set of free varables contans the varables set of consdered predcate wll be found. Step3. [Elnaton of the negaton nodes fro eleent calculus tree] For each negaton do For each node apply the negaton operaton. For the xpath nodes do: If there s a predcate n xpath negate the predcate, else ove to the root fro the current leaf untl the frst or node wll be reached, delete the subtree contanng the consdered leave, delete or node and lnk t wth the other subtree of ts parent. Creaton of Eleent Algebra tree. Algorth: Algebra (ECTree, EATree) Input: ECTree - an Eleent Calculus tree. Output: EATree - an Eleent Algebra tree. [Tree traversal] (a) If ( pe )( x1, x2,..., xk ( pe) = [/ //] Step0 [/ //] Step1... [/ //] Stepn Step = ( f condton not gve π L ( Step else +1 ( ) f condton s sple predcate π σ ( ) else σ γ ( Step ) L ρ Step = 0,1,..., n 1, Step0 = ntal xd eleent. (b) If ω( y 1, y2,..., y ) = ω1( u1, un ) ω2( v1, vl ) 1. If { u 1,,..., un} { v1,,..., vl } = A lg ebra( ω 1( u1,,..., un Alg ebra( ω2 ( v1,,..., v l 2. If { u 1,,..., u n } = { v1,,..., v l } A lg ebra( ω1( u1,,..., un Alg ebra( ω2 ( v1,,..., v l 3. If { u 1, u n } { v1, v l }, { v1, v l } { u1, u n } { u 1,,..., un} { v1,,..., vl } = v l 4. If { u 1, u n } { v1, v l } v l 5. If { v 1, v l } { u1, u n } v l (c) If ω( y y,..., y ) = ω ( u, u,..., u ) ( v, v,..., v ) and 1, 2 1 n ω2 l L ( ( ) π ρ, L

5 π y, y,..., y ( Alg ebra( ω ( u, u,..., un ) y y y ( Alg ebra( ( v, v,..., vl ) 1 π 1, 2,..., ω 2 (d) If ω ( y1, y2,..., y ) = ( y+ 1) ω( y1, y2,..., y, y+ 1) π y,,..., ( lg ( ( 1, 2,...,, 1) 1 y2 y A ebra ω y y y y + (e) If ω ( y1, y2,..., y ) = ( y+ 1) ω( y1, y2,..., y, y+ 1) Alg ebra( ω ( y1, y2,..., y, y+ 1) ) π y ( Alg ebra( ω( y1, y2,..., y, 1) +1 y + (f) If groupby( y1, y2,..., y, ω ( y1, y2,..., y, u1,,..., un ( x) γ x x x ( Alg ebra( ( y, y,..., y, u, u,..., un ) 1, 2,..., ω k (g) If orderby( y order, y order,..., y order, ω ( y, y,..., y 1 2 l τ y y y ( A ebra( ( y y y ), lg,,...,,..., ω l (h) If dstnct ( ω ( y y,..., δ ( A ebra( ω( y, y,..., ) () If 1, 2 y dentfca tonlst ρ (j) If predcate (k) If assgnent Algorth Correctness σ predcate π assgnentattrbutes lg The correctness of the suggested algorth s proved n the sae way as correctness of Codd s algorth [2] for reducton the relaton calculus to relatonal algebra as: Op ψ = Op ψ, Op wth, dstnct, groupby, orderby, assgnent. ( ) ( ) { } References 1. D. Beech, A. Malhotra & M. Rys (eds.) A foral data odel and algebra for XML. Councaton W3C, E. F. Codd. Relatonal Copleteness of Data Base Sublanguages, Data Base Systes, R. Rustn (ed. Prentce Hall, F. Frasncar, Geert-Jan Houben, C. Pau. XAL: an Algebra for XML Query Optzaton. In Proc. of 13th Australasan Database Conference, H. Garca-Molna, J. Ullan, J. Wdo, Database Systes: The Coplete Book, Prentce Hall, H. V. Jagadsh, et. al. TAX: A Tree Algebra for XML, In Proc. DBLP Conference Italy, M.G.Manukyan, Extensble Data Model, In Proc. of ADBIS 08 Conference, pp , Manukyan M. G., Eleent Algebra. In Proc. of ADBIS'09, LNCS 5968, Sprnger, pp , J. Shanugasundara, K. Tufte, G. He, C. Zhang, D. DeWtt, and J. Naughton, Relatonal Databases for Querng XML Docuents: Ltatons and Opportuntes. In Proc. VLDB Conference, pp , S. D. Vglas, et. al. Puttng XML Query Algebras nto Context, 2002, M. Zhang, J. T. Yao. XML Algebra for Data Mnng, In Proc. SPIE'04, y

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e. SSTEM MODELLIN In order to solve a control syste proble, the descrptons of the syste and ts coponents ust be put nto a for sutable for analyss and evaluaton. The followng ethods can be used to odel physcal

More information

Excess Error, Approximation Error, and Estimation Error

Excess Error, Approximation Error, and Estimation Error E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple

More information

Computational and Statistical Learning theory Assignment 4

Computational and Statistical Learning theory Assignment 4 Coputatonal and Statstcal Learnng theory Assgnent 4 Due: March 2nd Eal solutons to : karthk at ttc dot edu Notatons/Defntons Recall the defnton of saple based Radeacher coplexty : [ ] R S F) := E ɛ {±}

More information

04 - Treaps. Dr. Alexander Souza

04 - Treaps. Dr. Alexander Souza Algorths Theory 04 - Treaps Dr. Alexander Souza The dctonary proble Gven: Unverse (U,

More information

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form SET OF METHODS FO SOUTION THE AUHY POBEM FO STIFF SYSTEMS OF ODINAY DIFFEENTIA EUATIONS AF atypov and YuV Nulchev Insttute of Theoretcal and Appled Mechancs SB AS 639 Novosbrs ussa Introducton A constructon

More information

The Parity of the Number of Irreducible Factors for Some Pentanomials

The Parity of the Number of Irreducible Factors for Some Pentanomials The Party of the Nuber of Irreducble Factors for Soe Pentanoals Wolfra Koepf 1, Ryul K 1 Departent of Matheatcs Unversty of Kassel, Kassel, F. R. Gerany Faculty of Matheatcs and Mechancs K Il Sung Unversty,

More information

1 Definition of Rademacher Complexity

1 Definition of Rademacher Complexity COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #9 Scrbe: Josh Chen March 5, 2013 We ve spent the past few classes provng bounds on the generalzaton error of PAClearnng algorths for the

More information

ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES

ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Journal of Algebra, Nuber Theory: Advances and Applcatons Volue 3, Nuber, 05, Pages 3-8 ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Feldstrasse 45 CH-8004, Zürch Swtzerland e-al: whurlann@bluewn.ch

More information

Quantum Particle Motion in Physical Space

Quantum Particle Motion in Physical Space Adv. Studes Theor. Phys., Vol. 8, 014, no. 1, 7-34 HIKARI Ltd, www.-hkar.co http://dx.do.org/10.1988/astp.014.311136 Quantu Partcle Moton n Physcal Space A. Yu. Saarn Dept. of Physcs, Saara State Techncal

More information

find (x): given element x, return the canonical element of the set containing x;

find (x): given element x, return the canonical element of the set containing x; COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:

More information

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA A CLASS OF RECURSIVE SETS Florentn Smarandache Unversty of New Mexco 200 College Road Gallup, NM 87301, USA E-mal: smarand@unmedu In ths artcle one bulds a class of recursve sets, one establshes propertes

More information

On the set of natural numbers

On the set of natural numbers On the set of natural numbers by Jalton C. Ferrera Copyrght 2001 Jalton da Costa Ferrera Introducton The natural numbers have been understood as fnte numbers, ths wor tres to show that the natural numbers

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

Finite Fields and Their Applications

Finite Fields and Their Applications Fnte Felds and Ther Applcatons 5 009 796 807 Contents lsts avalable at ScenceDrect Fnte Felds and Ther Applcatons www.elsever.co/locate/ffa Typcal prtve polynoals over nteger resdue rngs Tan Tan a, Wen-Feng

More information

Xiangwen Li. March 8th and March 13th, 2001

Xiangwen Li. March 8th and March 13th, 2001 CS49I Approxaton Algorths The Vertex-Cover Proble Lecture Notes Xangwen L March 8th and March 3th, 00 Absolute Approxaton Gven an optzaton proble P, an algorth A s an approxaton algorth for P f, for an

More information

NOTES FOR QUANTUM GROUPS, CRYSTAL BASES AND REALIZATION OF ŝl(n)-modules

NOTES FOR QUANTUM GROUPS, CRYSTAL BASES AND REALIZATION OF ŝl(n)-modules NOTES FOR QUANTUM GROUPS, CRYSTAL BASES AND REALIZATION OF ŝl(n)-modules EVAN WILSON Quantum groups Consder the Le algebra sl(n), whch s the Le algebra over C of n n trace matrces together wth the commutator

More information

Applied Mathematics Letters

Applied Mathematics Letters Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć

More information

6. Stochastic processes (2)

6. Stochastic processes (2) Contents Markov processes Brth-death processes Lect6.ppt S-38.45 - Introducton to Teletraffc Theory Sprng 5 Markov process Consder a contnuous-tme and dscrete-state stochastc process X(t) wth state space

More information

6. Stochastic processes (2)

6. Stochastic processes (2) 6. Stochastc processes () Lect6.ppt S-38.45 - Introducton to Teletraffc Theory Sprng 5 6. Stochastc processes () Contents Markov processes Brth-death processes 6. Stochastc processes () Markov process

More information

Appendix B. Criterion of Riemann-Stieltjes Integrability

Appendix B. Criterion of Riemann-Stieltjes Integrability Appendx B. Crteron of Remann-Steltes Integrablty Ths note s complementary to [R, Ch. 6] and [T, Sec. 3.5]. The man result of ths note s Theorem B.3, whch provdes the necessary and suffcent condtons for

More information

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18 Multpont Analyss for Sblng ars Bostatstcs 666 Lecture 8 revously Lnkage analyss wth pars of ndvduals Non-paraetrc BS Methods Maxu Lkelhood BD Based Method ossble Trangle Constrant AS Methods Covered So

More information

On Pfaff s solution of the Pfaff problem

On Pfaff s solution of the Pfaff problem Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of

More information

COS 511: Theoretical Machine Learning

COS 511: Theoretical Machine Learning COS 5: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #0 Scrbe: José Sões Ferrera March 06, 203 In the last lecture the concept of Radeacher coplexty was ntroduced, wth the goal of showng that

More information

Gradient Descent Learning and Backpropagation

Gradient Descent Learning and Backpropagation Artfcal Neural Networks (art 2) Chrstan Jacob Gradent Descent Learnng and Backpropagaton CSC 533 Wnter 200 Learnng by Gradent Descent Defnton of the Learnng roble Let us start wth the sple case of lnear

More information

1 Matrix representations of canonical matrices

1 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 information

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

More information

Lecture 21: Numerical methods for pricing American type derivatives

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

More information

Two Conjectures About Recency Rank Encoding

Two Conjectures About Recency Rank Encoding Internatonal Journal of Matheatcs and Coputer Scence, 0(205, no. 2, 75 84 M CS Two Conjectures About Recency Rank Encodng Chrs Buhse, Peter Johnson, Wlla Lnz 2, Matthew Spson 3 Departent of Matheatcs and

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 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

Multiplicative Functions and Möbius Inversion Formula

Multiplicative Functions and Möbius Inversion Formula Multplcatve Functons and Möbus Inverson Forula Zvezdelna Stanova Bereley Math Crcle Drector Mlls College and UC Bereley 1. Multplcatve Functons. Overvew Defnton 1. A functon f : N C s sad to be arthetc.

More information

Chapter 13. Gas Mixtures. Study Guide in PowerPoint. Thermodynamics: An Engineering Approach, 5th edition by Yunus A. Çengel and Michael A.

Chapter 13. Gas Mixtures. Study Guide in PowerPoint. Thermodynamics: An Engineering Approach, 5th edition by Yunus A. Çengel and Michael A. Chapter 3 Gas Mxtures Study Gude n PowerPont to accopany Therodynacs: An Engneerng Approach, 5th edton by Yunus A. Çengel and Mchael A. Boles The dscussons n ths chapter are restrcted to nonreactve deal-gas

More information

What is LP? LP is an optimization technique that allocates limited resources among competing activities in the best possible manner.

What is LP? LP is an optimization technique that allocates limited resources among competing activities in the best possible manner. (C) 998 Gerald B Sheblé, all rghts reserved Lnear Prograng Introducton Contents I. What s LP? II. LP Theor III. The Splex Method IV. Refneents to the Splex Method What s LP? LP s an optzaton technque that

More information

XII.3 The EM (Expectation-Maximization) Algorithm

XII.3 The EM (Expectation-Maximization) Algorithm XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles

More information

Time and Space Complexity Reduction of a Cryptanalysis Algorithm

Time and Space Complexity Reduction of a Cryptanalysis Algorithm Te and Space Coplexty Reducton of a Cryptanalyss Algorth Mohaad Ghasezadeh Electrcal and Coputer Engneerng Departent, Yazd Unversty, Yazd, Iran.ghasezadeh@yazdun.ac.r Receved: /4/6; Accepted: /5/4 Pages:

More information

Designing Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate

Designing Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate The Frst Internatonal Senar on Scence and Technology, Islac Unversty of Indonesa, 4-5 January 009. Desgnng Fuzzy Te Seres odel Usng Generalzed Wang s ethod and Its applcaton to Forecastng Interest Rate

More information

APPENDIX A Some Linear Algebra

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

More information

Ballot Paths Avoiding Depth Zero Patterns

Ballot Paths Avoiding Depth Zero Patterns Ballot Paths Avodng Depth Zero Patterns Henrch Nederhausen and Shaun Sullvan Florda Atlantc Unversty, Boca Raton, Florda nederha@fauedu, ssull21@fauedu 1 Introducton In a paper by Sapounaks, Tasoulas,

More information

A Radon-Nikodym Theorem for Completely Positive Maps

A Radon-Nikodym Theorem for Completely Positive Maps A Radon-Nody Theore for Copletely Postve Maps V P Belavn School of Matheatcal Scences, Unversty of Nottngha, Nottngha NG7 RD E-al: vpb@aths.nott.ac.u and P Staszews Insttute of Physcs, Ncholas Coperncus

More information

Solving Fuzzy Linear Programming Problem With Fuzzy Relational Equation Constraint

Solving Fuzzy Linear Programming Problem With Fuzzy Relational Equation Constraint Intern. J. Fuzz Maeatcal Archve Vol., 0, -0 ISSN: 0 (P, 0 0 (onlne Publshed on 0 Septeber 0 www.researchasc.org Internatonal Journal of Solvng Fuzz Lnear Prograng Proble W Fuzz Relatonal Equaton Constrant

More information

Finite Vector Space Representations Ross Bannister Data Assimilation Research Centre, Reading, UK Last updated: 2nd August 2003

Finite Vector Space Representations Ross Bannister Data Assimilation Research Centre, Reading, UK Last updated: 2nd August 2003 Fnte Vector Space epresentatons oss Bannster Data Asslaton esearch Centre, eadng, UK ast updated: 2nd August 2003 Contents What s a lnear vector space?......... 1 About ths docuent............ 2 1. Orthogonal

More information

Solutions for Homework #9

Solutions for Homework #9 Solutons for Hoewor #9 PROBEM. (P. 3 on page 379 n the note) Consder a sprng ounted rgd bar of total ass and length, to whch an addtonal ass s luped at the rghtost end. he syste has no dapng. Fnd the natural

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 1998-2014. All Rghts Reserved. Created: July 15, 1999 Last Modfed: February 9, 2008 Contents 1 Lnear Fttng

More information

Canonical transformations

Canonical 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 information

On the number of regions in an m-dimensional space cut by n hyperplanes

On the number of regions in an m-dimensional space cut by n hyperplanes 6 On the nuber of regons n an -densonal space cut by n hyperplanes Chungwu Ho and Seth Zeran Abstract In ths note we provde a unfor approach for the nuber of bounded regons cut by n hyperplanes n general

More information

Introducing Entropy Distributions

Introducing Entropy Distributions Graubner, Schdt & Proske: Proceedngs of the 6 th Internatonal Probablstc Workshop, Darstadt 8 Introducng Entropy Dstrbutons Noel van Erp & Peter van Gelder Structural Hydraulc Engneerng and Probablstc

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

An Optimal Bound for Sum of Square Roots of Special Type of Integers

An Optimal Bound for Sum of Square Roots of Special Type of Integers The Sxth Internatonal Syposu on Operatons Research and Its Applcatons ISORA 06 Xnang, Chna, August 8 12, 2006 Copyrght 2006 ORSC & APORC pp. 206 211 An Optal Bound for Su of Square Roots of Specal Type

More information

Special Relativity and Riemannian Geometry. Department of Mathematical Sciences

Special Relativity and Riemannian Geometry. Department of Mathematical Sciences Tutoral Letter 06//018 Specal Relatvty and Reannan Geoetry APM3713 Seester Departent of Matheatcal Scences IMPORTANT INFORMATION: Ths tutoral letter contans the solutons to Assgnent 06. BAR CODE Learn

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

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

More information

Week 2. This week, we covered operations on sets and cardinality.

Week 2. This week, we covered operations on sets and cardinality. Week 2 Ths week, we covered operatons on sets and cardnalty. Defnton 0.1 (Correspondence). A correspondence between two sets A and B s a set S contaned n A B = {(a, b) a A, b B}. A correspondence from

More information

Revision: December 13, E Main Suite D Pullman, WA (509) Voice and Fax

Revision: December 13, E Main Suite D Pullman, WA (509) Voice and Fax .9.1: AC power analyss Reson: Deceber 13, 010 15 E Man Sute D Pullan, WA 99163 (509 334 6306 Voce and Fax Oerew n chapter.9.0, we ntroduced soe basc quanttes relate to delery of power usng snusodal sgnals.

More information

Representation theory and quantum mechanics tutorial Representation theory and quantum conservation laws

Representation theory and quantum mechanics tutorial Representation theory and quantum conservation laws Representaton theory and quantum mechancs tutoral Representaton theory and quantum conservaton laws Justn Campbell August 1, 2017 1 Generaltes on representaton theory 1.1 Let G GL m (R) be a real algebrac

More information

AN ANALYSIS OF A FRACTAL KINETICS CURVE OF SAVAGEAU

AN ANALYSIS OF A FRACTAL KINETICS CURVE OF SAVAGEAU AN ANALYI OF A FRACTAL KINETIC CURE OF AAGEAU by John Maloney and Jack Hedel Departent of Matheatcs Unversty of Nebraska at Oaha Oaha, Nebraska 688 Eal addresses: aloney@unoaha.edu, jhedel@unoaha.edu Runnng

More information

Turing Machines (intro)

Turing Machines (intro) CHAPTER 3 The Church-Turng Thess Contents Turng Machnes defntons, examples, Turng-recognzable and Turng-decdable languages Varants of Turng Machne Multtape Turng machnes, non-determnstc Turng Machnes,

More information

1 Generating functions, continued

1 Generating functions, continued Generatng functons, contnued. Exponental generatng functons and set-parttons At ths pont, we ve come up wth good generatng-functon dscussons based on 3 of the 4 rows of our twelvefold way. Wll our nteger-partton

More information

PARTITIONS WITH PARTS IN A FINITE SET

PARTITIONS WITH PARTS IN A FINITE SET PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volue 28, Nuber 5, Pages 269 273 S 0002-9939(0005606-9 Artcle electroncally publshe on February 7, 2000 PARTITIONS WITH PARTS IN A FINITE SET MELVYN B.

More information

Lecture 12: Discrete Laplacian

Lecture 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 information

Chapter 12 Lyes KADEM [Thermodynamics II] 2007

Chapter 12 Lyes KADEM [Thermodynamics II] 2007 Chapter 2 Lyes KDEM [Therodynacs II] 2007 Gas Mxtures In ths chapter we wll develop ethods for deternng therodynac propertes of a xture n order to apply the frst law to systes nvolvng xtures. Ths wll be

More information

Slobodan Lakić. Communicated by R. Van Keer

Slobodan Lakić. Communicated by R. Van Keer Serdca Math. J. 21 (1995), 335-344 AN ITERATIVE METHOD FOR THE MATRIX PRINCIPAL n-th ROOT Slobodan Lakć Councated by R. Van Keer In ths paper we gve an teratve ethod to copute the prncpal n-th root and

More information

Math Review. CptS 223 Advanced Data Structures. Larry Holder School of Electrical Engineering and Computer Science Washington State University

Math Review. CptS 223 Advanced Data Structures. Larry Holder School of Electrical Engineering and Computer Science Washington State University Math Revew CptS 223 dvanced Data Structures Larry Holder School of Electrcal Engneerng and Computer Scence Washngton State Unversty 1 Why do we need math n a data structures course? nalyzng data structures

More information

1. Statement of the problem

1. Statement of the problem Volue 14, 010 15 ON THE ITERATIVE SOUTION OF A SYSTEM OF DISCRETE TIMOSHENKO EQUATIONS Peradze J. and Tsklaur Z. I. Javakhshvl Tbls State Uversty,, Uversty St., Tbls 0186, Georga Georgan Techcal Uversty,

More information

Affine transformations and convexity

Affine 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 information

arxiv: v2 [math.co] 3 Sep 2017

arxiv: v2 [math.co] 3 Sep 2017 On the Approxate Asyptotc Statstcal Independence of the Peranents of 0- Matrces arxv:705.0868v2 ath.co 3 Sep 207 Paul Federbush Departent of Matheatcs Unversty of Mchgan Ann Arbor, MI, 4809-043 Septeber

More information

NP-Completeness : Proofs

NP-Completeness : Proofs NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem

More information

International Journal of Mathematical Archive-9(3), 2018, Available online through ISSN

International Journal of Mathematical Archive-9(3), 2018, Available online through   ISSN Internatonal Journal of Matheatcal Archve-9(3), 208, 20-24 Avalable onlne through www.ja.nfo ISSN 2229 5046 CONSTRUCTION OF BALANCED INCOMPLETE BLOCK DESIGNS T. SHEKAR GOUD, JAGAN MOHAN RAO M AND N.CH.

More information

SL n (F ) Equals its Own Derived Group

SL 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 information

DISCRIMINANTS AND RAMIFIED PRIMES. 1. Introduction A prime number p is said to be ramified in a number field K if the prime ideal factorization

DISCRIMINANTS AND RAMIFIED PRIMES. 1. Introduction A prime number p is said to be ramified in a number field K if the prime ideal factorization DISCRIMINANTS AND RAMIFIED PRIMES KEITH CONRAD 1. Introducton A prme number p s sad to be ramfed n a number feld K f the prme deal factorzaton (1.1) (p) = po K = p e 1 1 peg g has some e greater than 1.

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.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 information

Outline. Bayesian Networks: Maximum Likelihood Estimation and Tree Structure Learning. Our Model and Data. Outline

Outline. Bayesian Networks: Maximum Likelihood Estimation and Tree Structure Learning. Our Model and Data. Outline Outlne Bayesan Networks: Maxmum Lkelhood Estmaton and Tree Structure Learnng Huzhen Yu janey.yu@cs.helsnk.f Dept. Computer Scence, Unv. of Helsnk Probablstc Models, Sprng, 200 Notces: I corrected a number

More information

Need for Probabilistic Reasoning. Raymond J. Mooney. Conditional Probability. Axioms of Probability Theory. Classification (Categorization)

Need for Probabilistic Reasoning. Raymond J. Mooney. Conditional Probability. Axioms of Probability Theory. Classification (Categorization) Need for Probablstc Reasonng CS 343: Artfcal Intelence Probablstc Reasonng and Naïve Bayes Rayond J. Mooney Unversty of Texas at Austn Most everyday reasonng s based on uncertan evdence and nferences.

More information

More metrics on cartesian products

More 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 information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Problem Set 6: Trees Spring 2018

Problem Set 6: Trees Spring 2018 Problem Set 6: Trees 1-29 Sprng 2018 A Average dstance Gven a tree, calculate the average dstance between two vertces n the tree. For example, the average dstance between two vertces n the followng tree

More information

Differential Polynomials

Differential Polynomials JASS 07 - Polynomals: Ther Power and How to Use Them Dfferental Polynomals Stephan Rtscher March 18, 2007 Abstract Ths artcle gves an bref ntroducton nto dfferental polynomals, deals and manfolds and ther

More information

10-701/ Machine Learning, Fall 2005 Homework 3

10-701/ Machine Learning, Fall 2005 Homework 3 10-701/15-781 Machne Learnng, Fall 2005 Homework 3 Out: 10/20/05 Due: begnnng of the class 11/01/05 Instructons Contact questons-10701@autonlaborg for queston Problem 1 Regresson and Cross-valdaton [40

More information

An Introduction to Morita Theory

An Introduction to Morita Theory An Introducton to Morta Theory Matt Booth October 2015 Nov. 2017: made a few revsons. Thanks to Nng Shan for catchng a typo. My man reference for these notes was Chapter II of Bass s book Algebrac K-Theory

More information

INPUT-OUTPUT PAIRING OF MULTIVARIABLE PREDICTIVE CONTROL

INPUT-OUTPUT PAIRING OF MULTIVARIABLE PREDICTIVE CONTROL INPUT-OUTPUT PAIRING OF MULTIVARIABLE PREDICTIVE CONTROL Lng-Cong Chen #, Pu Yuan*, Gu-L Zhang* *Unversty of Petroleu, P.O. Box 902 Beng 00083, Chna # GAIN Tech Co., P.O. Box 902ext.79, Beng 00083, Chna

More information

Descriptional Complexity of Determinization and Complementation for Finite Automata

Descriptional Complexity of Determinization and Complementation for Finite Automata Descrptonal Complexty of Determnzaton and Complementaton for Fnte Automata Anruddh Gandh Nan Rosemary Ke Bakhadyr Khoussanov Department of Computer Scence, Unversty of Auckland Prvate Bag 99, Auckland,

More information

Lecture 3 January 31, 2017

Lecture 3 January 31, 2017 CS 224: Advanced Algorthms Sprng 207 Prof. Jelan Nelson Lecture 3 January 3, 207 Scrbe: Saketh Rama Overvew In the last lecture we covered Y-fast tres and Fuson Trees. In ths lecture we start our dscusson

More information

Compilers. Spring term. Alfonso Ortega: Enrique Alfonseca: Chapter 4: Syntactic analysis

Compilers. Spring term. Alfonso Ortega: Enrique Alfonseca: Chapter 4: Syntactic analysis Complers Sprng term Alfonso Ortega: alfonso.ortega@uam.es nrque Alfonseca: enrque.alfonseca@uam.es Chapter : Syntactc analyss. Introducton. Bottom-up Analyss Syntax Analyser Concepts It analyses the context-ndependent

More information

CHAPTER 10 ROTATIONAL MOTION

CHAPTER 10 ROTATIONAL MOTION CHAPTER 0 ROTATONAL MOTON 0. ANGULAR VELOCTY Consder argd body rotates about a fxed axs through pont O n x-y plane as shown. Any partcle at pont P n ths rgd body rotates n a crcle of radus r about O. The

More information

Complete subgraphs in multipartite graphs

Complete subgraphs in multipartite graphs Complete subgraphs n multpartte graphs FLORIAN PFENDER Unverstät Rostock, Insttut für Mathematk D-18057 Rostock, Germany Floran.Pfender@un-rostock.de Abstract Turán s Theorem states that every graph G

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

Introductory Cardinality Theory Alan Kaylor Cline

Introductory Cardinality Theory Alan Kaylor Cline Introductory Cardnalty Theory lan Kaylor Clne lthough by name the theory of set cardnalty may seem to be an offshoot of combnatorcs, the central nterest s actually nfnte sets. Combnatorcs deals wth fnte

More information

On quasiperfect numbers

On quasiperfect numbers Notes on Number Theory and Dscrete Mathematcs Prnt ISSN 1310 5132, Onlne ISSN 2367 8275 Vol. 23, 2017, No. 3, 73 78 On quasperfect numbers V. Sva Rama Prasad 1 and C. Suntha 2 1 Nalla Malla Reddy Engneerng

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The 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 information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Introduction to Algorithms

Introduction to Algorithms Introducton to Algorthms 6.046J/8.40J Lecture 7 Prof. Potr Indyk Data Structures Role of data structures: Encapsulate data Support certan operatons (e.g., INSERT, DELETE, SEARCH) Our focus: effcency of

More information

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Solutions 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 information

ALGEBRA MID-TERM. 1 Suppose I is a principal ideal of the integral domain R. Prove that the R-module I R I has no non-zero torsion elements.

ALGEBRA MID-TERM. 1 Suppose I is a principal ideal of the integral domain R. Prove that the R-module I R I has no non-zero torsion elements. ALGEBRA MID-TERM CLAY SHONKWILER 1 Suppose I s a prncpal deal of the ntegral doman R. Prove that the R-module I R I has no non-zero torson elements. Proof. Note, frst, that f I R I has no non-zero torson

More information

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2)

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2) 1/16 MATH 829: Introducton to Data Mnng and Analyss The EM algorthm (part 2) Domnque Gullot Departments of Mathematcal Scences Unversty of Delaware Aprl 20, 2016 Recall 2/16 We are gven ndependent observatons

More information

Graph Reconstruction by Permutations

Graph Reconstruction by Permutations Graph Reconstructon by Permutatons Perre Ille and Wllam Kocay* Insttut de Mathémathques de Lumny CNRS UMR 6206 163 avenue de Lumny, Case 907 13288 Marselle Cedex 9, France e-mal: lle@ml.unv-mrs.fr Computer

More information

CHAPTER 6 CONSTRAINED OPTIMIZATION 1: K-T CONDITIONS

CHAPTER 6 CONSTRAINED OPTIMIZATION 1: K-T CONDITIONS Chapter 6: Constraned Optzaton CHAPER 6 CONSRAINED OPIMIZAION : K- CONDIIONS Introducton We now begn our dscusson of gradent-based constraned optzaton. Recall that n Chapter 3 we looked at gradent-based

More information

Implicit Integration Henyey Method

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

More information

Preference and Demand Examples

Preference and Demand Examples Dvson of the Huantes and Socal Scences Preference and Deand Exaples KC Border October, 2002 Revsed Noveber 206 These notes show how to use the Lagrange Karush Kuhn Tucker ultpler theores to solve the proble

More information

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 )

Yong 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 information

The Impact of the Earth s Movement through the Space on Measuring the Velocity of Light

The Impact of the Earth s Movement through the Space on Measuring the Velocity of Light Journal of Appled Matheatcs and Physcs, 6, 4, 68-78 Publshed Onlne June 6 n ScRes http://wwwscrporg/journal/jap http://dxdoorg/436/jap646 The Ipact of the Earth s Moeent through the Space on Measurng the

More information

ON SEPARATING SETS OF WORDS IV

ON SEPARATING SETS OF WORDS IV ON SEPARATING SETS OF WORDS IV V. FLAŠKA, T. KEPKA AND J. KORTELAINEN Abstract. Further propertes of transtve closures of specal replacement relatons n free monods are studed. 1. Introducton Ths artcle

More information

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP C O L L O Q U I U M M A T H E M A T I C U M VOL. 80 1999 NO. 1 FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP BY FLORIAN K A I N R A T H (GRAZ) Abstract. Let H be a Krull monod wth nfnte class

More information