Outline. Midterm Review. Declaring Variables. Main Variable Data Types. Symbolic Constants. Arithmetic Operators. Midterm Review March 24, 2014

Size: px
Start display at page:

Download "Outline. Midterm Review. Declaring Variables. Main Variable Data Types. Symbolic Constants. Arithmetic Operators. Midterm Review March 24, 2014"

Transcription

1 Mdterm Revew March 4, 4 Mdterm Revew Larry Caretto Mechancal Engneerng 9 Numercal Analyss of Engneerng Systems March 4, 4 Outlne VBA and MATLAB codng Varable types Control structures (Loopng and Choce) Arrays, Functons and Subs Roots of Equatons General approach and two examples Matrx bascs and soluton by Gaussan elmnaton Numercal dfferentaton/interpolaton Man Varable Data Types Common VBA Varable types Data Type Memory Sze Range Boolean bytes TRUE (non-zero) or FALSE () Integer bytes -,678 to,767 Long (nteger) 4 bytes -,47,48,648 to,47,48,647 Sngle 4 bytes (+/-).498E-45 to.48e8 Double 8 bytes (+/-) E-4 to e8 Strng (varable-length) bytes + length to about bllon characters Date 8 bytes Date and tme of day Varant 6 bytes (more for strngs) Holds any data type (Default type) MATLAB uses double data type for numercal values and has strng data type All MATLAB varables can be complex arrays; no varable declaraton s requred Declarng Varables VBA uses the followng syntax Dm x As Double Dm k As Long For loop ndex Dm s As Strng, d as Date Dm y as Double, z As Double Do not use the followng syntax n whch x and y are type varant: Dm x, y, z As Double Declaratons not requred n MATLAB 4 Arthmetc Operators Operators for both VBA and MATLAB Exponentaton ^, Unary mnus (E.g. x), Multply/Dvde * / Addton/Subtracton + Both use parentheses to overrule normal rules of precedence MATLAB operators * / and ^ apply to arrays, but there are also term-by-term operators:.*./ and.^ MATLAB: A/B => AB - and A\B = A - B 5 Symbolc Constants Useful way to program tems that are constant or not expected to change often Syntax: Const PI as Double = A const cannot be assgned a value n any other statement Not avalable n MATLAB 6 ME 9 Numercal Analyss of Engneerng Systems

2 Mdterm Revew March 4, 4 Relatonal/Logcal Operators Program logc requres choces based on expressons that are true or false Relatonal operators compare other varables and have true or false results MATLAB and VBA use <, <=, =, >, >= Not equal s <> n VBA ~= n MATLAB VBA: Not, And, Or; MATLAB: ~, &&, MATLAB array comparsons return arrays of (true) and (false) and use ~ & 7 If Else If n VBA If <condton> Then <Statements done f condton s true> ElseIf <condton> Then <Statements done f condton s true> ElseIf <condton> Then <Statements done f condton s true> <May be other condtons> Else <Statements done f all condtons false> End If <Execute here after any statements done> 8 If Else If n MATLAB f <condton> <Statements done f condton s true> elsef <condton> <Statements done f condton s true> elsef <condton> <Statements done f condton s true> <May be other condtons> else <Statements done f all condtons false> end <Execute here after any statements done> 9 If Else If Explaned If any condton s true, the statements followng the If or ElseIf are executed Once those statements are executed controls to the frst statement after the End If Statements for only the frst true condton are executed The Else block s optonal If no condtons are true those statements are executed If <condton> The <Statements done ElseIf <condton> <Statements done ElseIf <condton> <Statements done <May be other cond Else <Statements done End If <Execute here after Loopng Count control loop repeats code a fxed number of tmes Condtonal loopng repeats whle a condton s true or untl a condton s false Both types of loops may be nested May use statements (VBA Ext For or Ext Do; MATLAB break) to ext loop before normal ext VBA Count Controlled Loop For <counter> = <start> to <end> <statements> If Step not specfed, Next <counter> <ncrement> = For <counter> = <start> to <end> _ Step <ncrement> <statements> Next <counter> Statements n loop repeated ntmes = Int((<end> <start>) /<ncrement>) + Loop not executed f ntmes <= ME 9 Numercal Analyss of Engneerng Systems

3 Mdterm Revew March 4, 4 MATLAB Count Controlled Loop for <counter> = <array> <statements> end Array may be set of values, e.g. for T = [ 7 6] Can use array defnton lke VBA for loop for <counter> = <start>:<ncrement>:<end> Statements n loop repeated for each element n array Note ablty to specfy non-unform array ncrement VBA Condtonal Loop <cond> s a condton (can be true or false) are statements executed n the loop (whch can change the condton) Do f <cond> _ Then Ext Do Loop Do Whle <cond> Loop Do Loop Whle <cond> Do Untl <cond> Loop Do Loop Untl <cond> Note tests before or after loop 4 MATLAB Condtonal Loop Only one loop, a whle loop, wth the followng structure whle <condton> <statements> end Example (compute machne epslon) eps = whle + eps ~= eps = eps / end eps = eps * 5 Arrays Arrays can be vsualzed as data on an expermental varable Could descrbe pressure data ponts mathematcally as P, P, etc. In programmng languages we can represent data ponts as P(), P(), etc. We call the numbers (,, etc.) ndces or subscrpts We can use constants or varables for the subscrpts: P(4), P(k), where k has a value 6 V() Two-dmensonal Arrays Consder an experment where you vary the current over sx levels, the voltage over four levels and measure the effcency, e, of an electromechancal devce. The data for each combnaton of current and voltage can be represented as shown below V() V() V(4) I() I() I() I(4) I(5) I(6) e(,) e(,) e(,) e(,4) e(,5) e(,6) e(,) e(,) e(,) e(,4) e(,5) e(,6) e(,) e(,) e(,) e(,4) e(,5) e(,6) e(4,) e(4,) e(4,) e(4,4) e(4,5) e(4,6) 7 Dmensonng Arrays n VBA Can declare arrays as follows Dm I( to 6) as double Dm V( to 4) as double Dm e( to 4, to 6) as double Sze below depends on Opton Base Dm I(6) as double Array Dm V(4) as double dmensonng not requred n Dm e(4, 6) as double MATLAB 8 ME 9 Numercal Analyss of Engneerng Systems

4 Mdterm Revew March 4, 4 Usng Arrays n VBA VBA array components are referenced by ther subscrpts Ths s often done n a for loop PI = 4 * atn() For k = to x(k) = sn(k * PI / ) Next k In MATLAB use: t = :p/:p x = sn(t) x s an array wth components gvng sn(x) for x p, wth Dx = p/ Two-Dmensonal Arrays n VBA Use nested for loops Use example of exstng data on current and voltages stored n arrays For k = to 4 For j = to 6 Power(k,j) = current(j) * voltage(k) Next j Next k 9 MATLAB Arrays Enter arrays as x = [ ; Elements n one row separated by spaces Semcolon ndcates new row Subarrays z = x(r:r,c:c); transpose x Array formula operators: +, -, *, /,\,^ gve matrx results (A/B = AB -, A\B = A - B) Term-by-term operatons wth +, -,.*,./,.^ Buld larger arrays from smaller arrays usng same approach enterng ntal array VBA Strngs Consder only varable length Use Dm str as Strng to dmenson strng Use & or + as concatenaton operator to jon two strngs Len(str) gves length of strng Left, Rght, and Md gve substrngs n same manner as worksheet functons InStr functon searches for substrngs Strng constant s = strng MATLAB Strngs Use sngle quotes for strng constants Settng s = strng makes s a strng varable wth the value strng No declaraton requred Concatenate strngs by placng them n an array of strngs s = [ Ths strng has characters ] result: s = Ths strng has characters VBA Functons The header has the followng form Functon <name> ( <arguments> ) As <type> <name> s the name of the functon Must set name to some value n functon code <type> s the data type for the functon <arguments> may be blank or have one or more entres of the form <varable> As <type> <varable> s a varable used n the functon <type> s the data type for that varable Separate multple entres n the lst by commas Arguments provde nput data to functon 4 ME 9 Numercal Analyss of Engneerng Systems 4

5 Mdterm Revew March 4, 4 Wrtng Your VBA Functon The functon has the followng form Functon <name> ( <arguments> ) As <type> <code to do computatons> <name> = <value from computatons> End Functon Example functon Functon vcyl ( R as Double, H as Double ) As _ Double vcyl = 4 * atn() * R^ * H End Functon Usng Your VBA Functon Use by cell entres n worksheet =vcyl( B, B) =vcyl(, ) =vcyl( radusname, heghtname) Call from other VBA procedures V = vcyl( radus, heght) cylvol = vcyl(, ) vcyls = * vcyl( rad, hgt) 5 6 MATLAB Functons The header has the followng form functon <return> = <name> ( <arguments> ) <return> s a one varable or row array of varables returned by the functon <name> s the name of the functon <arguments> may be blank or have one or more varable names separated by commas Arguments provde nput data to functon Varables n the <return> lst must be assgned a value n the functon 7 MATLAB Trajectory Functon functon [x, y] = traj(v, theta, N) %Computes frctonless trajectory Use fle %Uses SI unts (meters, seconds) names the %V s ntal speed n m/s same as the %theta s ntal angle n degrees functon %N s number of ponts computed g = ; %gravty n m/s^ names (e.g. tmax = * v * snd(theta) / g; traj.m) to t = :tmax/(n-):tmax; save x = v*cosd(theta) * t; functons y = v*snd(theta) * t - g * t.^ /; plot(x,y); end Operator.^ allows use of t as an array 8 Usng the Trajectory Functon Use only the functon name wll return value of frst argument, x, n default varable ans >> trajectory(, 45, ); Return x array only >>x = trajectory(, 45, ) Return x and y arrays >> [x,y] = trajectory(, 45, ) Roots of Equatons, f(x) = Plot of f (x) = x and f (x) = e x/ sn(4x) f(x) = x e x/ sn(4x) = Roots about x =, x =.8, x =.6, etc. Plot shows that ths equaton has nfnte number of roots 9 ME 9 Numercal Analyss of Engneerng Systems 5

6 Mdterm Revew March 4, 4 Iteraton Solutons In teraton we make one (or more) ntal guesses for x and compute f(x) x (m) or x m s value of x at teraton m f (m) or f m = f(x m ) s value of f(x) at x = x m Unless we are extremely lucky we wll not fnd f(x) = for ntal guesses Use recent value(s) of f(x) to estmate a value of x that gets us closer to the soluton x* at whch f = Convergence Crtera x + x e + e x + OR f + e combnes tests on x and f Useful when df/dx >> or df/dx << n the regon of the soluton Although we are tryng to solve for f =, we are really nterested n fndng the value of x Error tests on x are usually more mportant Relatve error test s more general Secant Method Operaton Algorthm: Make ntal guesses x and x Compute f = f(x ) and f = f(x ) Repeat x + = x f [(x x - ) / (f f - )] Untl convergence crteron s met Algorthm does not requre ntal guesses that bracket root, but bad ntal guesses may not converge quckly or at all Secant Method Example f(x) = e x x Intal guesses x = and x = f = f(x ) = f() = e = f = f(x ) = f() = e =.878 x = x f x x f f =.878 x =.9 f = f(x ) = e.9.99 =.656 x = x f x x f f = x =.99; f =.5 Root s Newton s Method Operaton Algorthm: Make ntal guess x Compute f = f(x ) and f (x ) = df/dx x=x Repeat x + = x f / f (x ) Untl convergence crteron s met Algorthm requres only one ntal guess A bad ntal guesses may not converge quckly or at all or converge to wrong root 5 Newton s Method Example f(x) = e x x f (x) = e x Intal guess x = (x = gves f = ) f = f(x ) = f() = e =.878 f (x) = e =.788 x = x f f (x ) = =.695 f x = e =.866 f x = e.65 =.569 x = x f.866 =.695 =.464 f (x ).569 Fnd x 4 =.4696, f() = 8.x -8 6 ME 9 Numercal Analyss of Engneerng Systems 6

7 Mdterm Revew March 4, 4 Matrx Bascs Matrx s array wth n rows and m columns A = B f same sze and a j = b j for all and j Add or subtract matrces C = A B only vald f A, B, and C have the same sze (rows and columns) Components of C, c j = a j b j Multplcaton by a scalar: C = xa C and A have the same sze (rows and columns) Components of C, c j = xa j For scalar dvson, C = A/x, c j = a j /x 7 Null () and Unt (I) Matrces For any matrx, A, A + = + A = A; IA = AI = A and A = A = The unt (or dentty) matrx s a square matrx; the null matrx, whch need not be square, s sometmes wrtten (nxm) I 8 Dagonal Matrx and Transpose Dagonal matrx, D, has nonzero terms only on dagonal Transpose of A, denoted as A T swtches rows and columns 6 A 4 d D T d d A 6 d n 4 9 General Matrx Multplcaton For matrx multplcaton, C = AB p A has n rows and p columns cj bkakj B has p rows and m columns k C has n rows and m columns (, n; j, m ) Example A 4 () () 6(6) AB 4() () (6) 4 6 B 6 (4) () 6() 7 4(4) () () 6 4 Inverse of a Matrx For a square matrx, A, an nverse matrx, A - may exst such that AA - = A - A = I For the algebrac equaton ax = b, x = a - b For the matrx equaton Ax = b, x = A - b Just as x = a - b s not vald f a =, x = A - b s not vald f A - does not exst A - does not exst f DetA = The nverse and the determnant are mportant concepts n analyss of lnear systems, but are not used n computatonal work 4 From Equatons to Ax = b Usual form for N = equatons x + 7y z = 8 x 4y + z = - 8x + 6y z = 4 A x = Ax = b 7 x x 7x x 8 4 x x 4x x 8 6 x x x x An equaton s a row n the Ax = b format 4 ME 9 Numercal Analyss of Engneerng Systems 7

8 Mdterm Revew March 4, 4 Gaussan Elmnaton Solve the set x 4x 6x 4 ( ) of equatons x 9 ( ) on the rght x x 7x 8 4 ( ) x x Subtract / tmes () from equaton () and 7/ tmes () from () x ( 4) x 9 ( 6) x ( 4) x ( 4) x 8 ( 6) x 4 ( 4) Gaussan Elmnaton II Result from frst set of operatons Subtract 7/(-4) tmes () from () Fnal uppertrangular form x 4x 6x 4 x 4x x 8 x 7x 99x 4 44 Unnecessary computer operatons 7 x 7 ( 4) x ( ) ( 8) 4 4 x 4x 6x 4 x 4x x 8 57 x x x 57 Fnal uppertrangular form Solve thrd equaton for x Back Substtuton x 4x 6x 4 4x x 8 57 x x 57 Solve second 8 x 8 x equaton for x 4 4 Solve frst equaton for x 4x 6x 4 4 6x x 4 45 General Gaussan Loop over all rows from to N to be used as the pvot row For each pvot row, loop over all rows from pvot + to N For each row loop over all columns from pvot+ to N + nbcols (work wth augmented matrx [A b] a row, pvot a row, column row, column apvot, column apvot, pvot n Back substtuton formula for = n, n-,, : a b x j j a a x 46 j Solutons for Ax = b For a set of m equatons n n unknowns If Rank(A) = Rank([A b]) = n, there s a unque soluton If Rank(A) = Rank([A b]) < n, there are an nfnte number of solutons If Rank(A) Rank([A b]) there are no solutons Use Gaussan elmnaton to fnd Rank as number of nonzero rows x 4x x 7x x x 4x x x x 6x 9x 8x x 4x x x x Three Examples 9x 6x 6x 5 5 6x 9x 6x x 4x 9x 5.5x x x 4x x x 4x x 6x 9x 6x 9x 6x 5 5 x x 7 x x 8 8 x x No soluton ME 9 Numercal Analyss of Engneerng Systems 8

9 Mdterm Revew March 4, 4 Frst Example Rank Second Example Rank Orgnal Rowechelon form A A b A 9 A b Here we see that rank(a) = rank([a b]) = number of unknowns = so we have a unque soluton Orgnal Rowechelon form A A b 4 6 A 9 A b rank(a) = rank([a b]) = whch s less than the number of unknowns () so we have an nfnte number of solutons 5 Orgnal Rowechelon form Thrd Example Rank A A b 4 6 A 9 A b 4 4 Here, rank(a) = rank([a b]) = ; therefore we have no solutons Numercal Dfferentaton Formulas have followng propertes Type of dervatve (frst, second, thrd, etc.) Locaton of ponts used n the dervatve, relatve to the pont of the dervatve (forward dfference, backward dfference, central dfference) Order of the error: O(h n ) s an n th order error (truncaton error proportonal to h n ) Roundoff error occurs when h s so small that sgnfcant fgures are lost 5 ' Some Dervatve Expressons f f f ' Oh h f f f h O h Note order of dervatve, order of error, and drecton (forward vs. backward) ' ' f f f h f f ' f f 4 f h 4 f h O h f f '' f f f f h O h O h O h O(h n ) for nformaton only. Not used n calculatons. 5 More Dervatve Expressons f = f 5f + 4f f h + O h f = f + + 8f + 8f + f + O h 4 h f = f + 6f f + 6f + + f + h + O h 4 What s order of dervatve, order of error, and drecton (central, forward or backward dfference ) of expresson? Is sum of coeffcents always zero? 54 ME 9 Numercal Analyss of Engneerng Systems 9

10 Mdterm Revew March 4, 4 Polynomal Interpolaton Ft n th order polynomal, p(x), to n + data ponts, (x, y ) to (x n, y n ) Can start at any data pont n set Select ponts for nterpolaton that are closest to the value to be nterpolated Basc dea s that polynomal wll ft each data pont exactly: p(x ) = y Example s Newton polynomal a + a (x x ) + a (x x )(x x ) + a k coeffcents from dvded-dfference table 55 Newton Polynomals from x start p (x) = a and p (x) = a + a (x x start ) p (x) = a + a (x x start ) + a (x x start ) (x x start+ ) p (x) = a + a (x x start ) + a (x x start ) (x x start+ ) + a (x x start ) (x x start+ )(x x start+ ) a = y start, a = F start = (y start+ y start ) / (x start+ x start ). a = S start = (F start+ n m pnx amx xstartk pnx pn x an x xstartk m k n k 56 x = Sample Dvded Dfference Table y = a Follow same a y y k pattern for F a other startng ponts x x y = F F S a x y y x F S S T a x x x x F F T T 5 y =5 S R x x x y y S S F T a x 4 x x F F 5 y = S x Dvded dfference y4 y table gves polynomal F x coeffcents (next 9 y 4 =79 page) 57 Dvded Dfference Table for p(x = ) X = y = 5 6 y y F F 5 5 x x F F y = a S x x F y y S 5 S F a T x x x x F F 5 y =5 S T T x a R x x y y S S 4 x F T x x x F F 5 y = S x y4 y F 4 6 x x S 9 y 4 = Quadratc Polynomal Usual form for ntal pont at x = x p (x) = a + a (x x ) + a (x x )(x x ) Here we start at x start = x p (x) = a + a (x x ) + a (x x )(x x ) Usng data from prevous chart gves 6 7 px x x x 5 5 Ths gves p(x k ) = y k for x k =, 5, and 5 Interpolated value for x = s Mdterm Exam Closed book (code sheet on exam shows basc VBA and MATLAB structures) Problems lke those on quzzes Interpretng VBA or MATLAB code Wrtng smple VBA or MATLAB code Usng a gven algorthm to solve f(x) = Smple matrx operatons (equalty, addton, subtracton, multplcaton, transpose) Smple Gaussan elmnaton (4 x 4 system) Recognze unque, zero, or nfnte solutons Numercal dfferentaton and nterpolaton 6 ME 9 Numercal Analyss of Engneerng Systems

11 Mdterm Revew March 4, 4 Sxth Quz Results Number of students: Maxmum Possble Score: 5 Average score: 8. Medan score:.5 Standard devaton: 6.7 Grade dstrbuton: Sxth Quz Comments Choose data ponts n the regon of the x value to be nterpolated Denomnators of dvded dfference expressons are always Dx s Choose central-dfferences dervatve expressons f possble; use one-sded dfferences for boundares only To fnd the n th dervatve of y n a table of y vs. x, use y values n the numerator 6 Sxth Quz Comments II O(h n ) s for nformaton about order of error only, not part of calculaton For dervatves from a data table h = D(ndependent varable) Numerator values are dependent varable Here x s ndependent, y dependent Asked to fnd second dervatve of y Do not have to get entre dvdeddfference table, only the terms needed Look for type of polynomal requred 6 ME 9 Numercal Analyss of Engneerng Systems

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Programming Assignment Six. Semester Calendar. 1D Excel Worksheet Arrays. Review VBA Arrays from Excel. Programming Assignment Six May 2, 2017

Programming Assignment Six. Semester Calendar. 1D Excel Worksheet Arrays. Review VBA Arrays from Excel. Programming Assignment Six May 2, 2017 Programmng Assgnment Sx, 07 Programmng Assgnment Sx Larry Caretto Mechancal Engneerng 09 Computer Programmng for Mechancal Engneers Outlne Practce quz for actual quz on Thursday Revew approach dscussed

More information

Solutions to Programming Assignment Five Interpolation and Numerical Differentiation

Solutions to Programming Assignment Five Interpolation and Numerical Differentiation College of Engneerng and Coputer Scence Mechancal Engneerng Departent Mechancal Engneerng 309 Nuercal Analyss of Engneerng Systes Sprng 04 Nuber: 537 Instructor: Larry Caretto Solutons to Prograng Assgnent

More information

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005 Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed

More information

Agenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals

Agenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals Agenda & Readng COMPSCI 8 SC Applcatons Programmng Programmng Fundamentals Control Flow Agenda: Decsonmakng statements: Smple If, Ifelse, nested felse, Select Case s Whle, DoWhle/Untl, For, For Each, Nested

More information

Brave New World Pseudocode Reference

Brave New World Pseudocode Reference Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016) Technsche Unverstät München WSe 6/7 Insttut für Informatk Prof. Dr. Thomas Huckle Dpl.-Math. Benjamn Uekermann Parallel Numercs Exercse : Prevous Exam Questons Precondtonng & Iteratve Solvers (From 6)

More information

Lecture 4: Principal components

Lecture 4: Principal components /3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

Very simple computational domains can be discretized using boundary-fitted structured meshes (also called grids)

Very simple computational domains can be discretized using boundary-fitted structured meshes (also called grids) Structured meshes Very smple computatonal domans can be dscretzed usng boundary-ftted structured meshes (also called grds) The grd lnes of a Cartesan mesh are parallel to one another Structured meshes

More information

Sorting and Algorithm Analysis

Sorting and Algorithm Analysis Unt 7 Sortng and Algorthm Analyss Computer Scence S-111 Harvard Unversty Davd G. Sullvan, Ph.D. Sortng an Array of Integers 0 1 2 n-2 n-1 arr 15 7 36 40 12 Ground rules: sort the values n ncreasng order

More information

News. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example

News. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example Unversty of Brtsh Columba CPSC, Intro to Computaton Jan-Apr Tamara Munzner News Assgnment correctons to ASCIIArtste.java posted defntely read WebCT bboards Arrays Lecture, Tue Feb based on sldes by Kurt

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references

More information

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place

More information

Machine Learning 9. week

Machine Learning 9. week Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below

More information

CS 534: Computer Vision Model Fitting

CS 534: Computer Vision Model Fitting CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust

More information

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson

More information

CHARUTAR VIDYA MANDAL S SEMCOM Vallabh Vidyanagar

CHARUTAR VIDYA MANDAL S SEMCOM Vallabh Vidyanagar CHARUTAR VIDYA MANDAL S SEMCOM Vallabh Vdyanagar Faculty Name: Am D. Trved Class: SYBCA Subject: US03CBCA03 (Advanced Data & Fle Structure) *UNIT 1 (ARRAYS AND TREES) **INTRODUCTION TO ARRAYS If we want

More information

Midterms Save the Dates!

Midterms Save the Dates! Unversty of Brtsh Columba CPSC, Intro to Computaton Alan J. Hu Readngs Ths Week: Ch 6 (Ch 7 n old 2 nd ed). (Remnder: Readngs are absolutely vtal for learnng ths stuff!) Thnkng About Loops Lecture 9 Some

More information

Lecture #15 Lecture Notes

Lecture #15 Lecture Notes Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal

More information

Polyhedral Compilation Foundations

Polyhedral Compilation Foundations Polyhedral Complaton Foundatons Lous-Noël Pouchet pouchet@cse.oho-state.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty Feb 8, 200 888., Class # Introducton: Polyhedral Complaton Foundatons

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR Judth Aronow Rchard Jarvnen Independent Consultant Dept of Math/Stat 559 Frost Wnona State Unversty Beaumont, TX 7776 Wnona, MN 55987 aronowju@hal.lamar.edu

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

MATHEMATICS FORM ONE SCHEME OF WORK 2004

MATHEMATICS FORM ONE SCHEME OF WORK 2004 MATHEMATICS FORM ONE SCHEME OF WORK 2004 WEEK TOPICS/SUBTOPICS LEARNING OBJECTIVES LEARNING OUTCOMES VALUES CREATIVE & CRITICAL THINKING 1 WHOLE NUMBER Students wll be able to: GENERICS 1 1.1 Concept of

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

Multiple optimum values

Multiple optimum values 1.204 Lecture 22 Unconstraned nonlnear optmzaton: Amoeba BFGS Lnear programmng: Glpk Multple optmum values A B C G E Z X F Y D X 1 X 2 Fgure by MIT OpenCourseWare. Heurstcs to deal wth multple optma: Start

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

AP PHYSICS B 2008 SCORING GUIDELINES

AP PHYSICS B 2008 SCORING GUIDELINES AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for

More information

Harvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6)

Harvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6) Harvard Unversty CS 101 Fall 2005, Shmon Schocken Assembler Elements of Computng Systems 1 Assembler (Ch. 6) Why care about assemblers? Because Assemblers employ some nfty trcks Assemblers are the frst

More information

Lecture 5: Probability Distributions. Random Variables

Lecture 5: Probability Distributions. Random Variables Lecture 5: Probablty Dstrbutons Random Varables Probablty Dstrbutons Dscrete Random Varables Contnuous Random Varables and ther Dstrbutons Dscrete Jont Dstrbutons Contnuous Jont Dstrbutons Independent

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss.

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss. Today s Outlne Sortng Chapter 7 n Wess CSE 26 Data Structures Ruth Anderson Announcements Wrtten Homework #6 due Frday 2/26 at the begnnng of lecture Proect Code due Mon March 1 by 11pm Today s Topcs:

More information

Oracle Database: SQL and PL/SQL Fundamentals Certification Course

Oracle Database: SQL and PL/SQL Fundamentals Certification Course Oracle Database: SQL and PL/SQL Fundamentals Certfcaton Course 1 Duraton: 5 Days (30 hours) What you wll learn: Ths Oracle Database: SQL and PL/SQL Fundamentals tranng delvers the fundamentals of SQL and

More information

Kinematics of pantograph masts

Kinematics of pantograph masts Abstract Spacecraft Mechansms Group, ISRO Satellte Centre, Arport Road, Bangalore 560 07, Emal:bpn@sac.ernet.n Flght Dynamcs Dvson, ISRO Satellte Centre, Arport Road, Bangalore 560 07 Emal:pandyan@sac.ernet.n

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Biostatistics 615/815

Biostatistics 615/815 The E-M Algorthm Bostatstcs 615/815 Lecture 17 Last Lecture: The Smplex Method General method for optmzaton Makes few assumptons about functon Crawls towards mnmum Some recommendatons Multple startng ponts

More information

Radial Basis Functions

Radial Basis Functions Radal Bass Functons Mesh Reconstructon Input: pont cloud Output: water-tght manfold mesh Explct Connectvty estmaton Implct Sgned dstance functon estmaton Image from: Reconstructon and Representaton of

More information

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables

More information

Classification / Regression Support Vector Machines

Classification / Regression Support Vector Machines Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM

More information

Intro. Iterators. 1. Access

Intro. Iterators. 1. Access Intro Ths mornng I d lke to talk a lttle bt about s and s. We wll start out wth smlartes and dfferences, then we wll see how to draw them n envronment dagrams, and we wll fnsh wth some examples. Happy

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

Loop Transformations, Dependences, and Parallelization

Loop Transformations, Dependences, and Parallelization Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 3-4:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Steve Setz Wnter 2009 Qucksort Qucksort uses a dvde and conquer strategy, but does not requre the O(N) extra space that MergeSort does. Here s the

More information

9. BASIC programming: Control and Repetition

9. BASIC programming: Control and Repetition Am: In ths lesson, you wll learn: H. 9. BASIC programmng: Control and Repetton Scenaro: Moz s showng how some nterestng patterns can be generated usng math. Jyot [after seeng the nterestng graphcs]: Usng

More information

Outline. Third Programming Project Two-Dimensional Arrays. Files You Can Download. Exercise 8 Linear Regression. General Regression

Outline. Third Programming Project Two-Dimensional Arrays. Files You Can Download. Exercise 8 Linear Regression. General Regression Project 3 Two-densonal arras Ma 9, 6 Thrd Prograng Project Two-Densonal Arras Larr Caretto Coputer Scence 6 Coputng n Engneerng and Scence Ma 9, 6 Outlne Quz three on Thursda for full lab perod See saple

More information

Sorting. Sorting. Why Sort? Consistent Ordering

Sorting. Sorting. Why Sort? Consistent Ordering Sortng CSE 6 Data Structures Unt 15 Readng: Sectons.1-. Bubble and Insert sort,.5 Heap sort, Secton..6 Radx sort, Secton.6 Mergesort, Secton. Qucksort, Secton.8 Lower bound Sortng Input an array A of data

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

Insertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array

Insertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array Inserton Sort Dvde and Conquer Sortng CSE 6 Data Structures Lecture 18 What f frst k elements of array are already sorted? 4, 7, 1, 5, 1, 16 We can shft the tal of the sorted elements lst down and then

More information

Review of approximation techniques

Review of approximation techniques CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated

More information

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis Internatonal Mathematcal Forum, Vol. 6,, no. 7, 8 Soltary and Travelng Wave Solutons to a Model of Long Range ffuson Involvng Flux wth Stablty Analyss Manar A. Al-Qudah Math epartment, Rabgh Faculty of

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky Improvng Low Densty Party Check Codes Over the Erasure Channel The Nelder Mead Downhll Smplex Method Scott Stransky Programmng n conjuncton wth: Bors Cukalovc 18.413 Fnal Project Sprng 2004 Page 1 Abstract

More information

LESSON 15: BODE PLOTS OF TRANSFER FUNCTIONS

LESSON 15: BODE PLOTS OF TRANSFER FUNCTIONS 10/8/015 1 LESSON 15: BODE PLOTS OF TRANSFER FUNCTIONS ET 438a Automatc Control Systems Technology Learnng Objectves After ths presentaton you wll be able to: Compute the magntude of a transfer functon

More information

Sorting: The Big Picture. The steps of QuickSort. QuickSort Example. QuickSort Example. QuickSort Example. Recursive Quicksort

Sorting: The Big Picture. The steps of QuickSort. QuickSort Example. QuickSort Example. QuickSort Example. Recursive Quicksort Sortng: The Bg Pcture Gven n comparable elements n an array, sort them n an ncreasng (or decreasng) order. Smple algorthms: O(n ) Inserton sort Selecton sort Bubble sort Shell sort Fancer algorthms: O(n

More information

Wishing you all a Total Quality New Year!

Wishing you all a Total Quality New Year! Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma

More information

Inverse Kinematics (part 2) CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Spring 2016

Inverse Kinematics (part 2) CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Spring 2016 Inverse Knematcs (part 2) CSE169: Computer Anmaton Instructor: Steve Rotenberg UCSD, Sprng 2016 Forward Knematcs We wll use the vector: Φ... 1 2 M to represent the array of M jont DOF values We wll also

More information

USING GRAPHING SKILLS

USING GRAPHING SKILLS Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned

More information

CS240: Programming in C. Lecture 12: Polymorphic Sorting

CS240: Programming in C. Lecture 12: Polymorphic Sorting CS240: Programmng n C ecture 12: Polymorphc Sortng Sortng Gven a collecton of tems and a total order over them, sort the collecton under ths order. Total order: every tem s ordered wth respect to every

More information

High level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization

High level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization What s a Computer Program? Descrpton of algorthms and data structures to acheve a specfc ojectve Could e done n any language, even a natural language lke Englsh Programmng language: A Standard notaton

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

Parameter estimation for incomplete bivariate longitudinal data in clinical trials

Parameter estimation for incomplete bivariate longitudinal data in clinical trials Parameter estmaton for ncomplete bvarate longtudnal data n clncal trals Naum M. Khutoryansky Novo Nordsk Pharmaceutcals, Inc., Prnceton, NJ ABSTRACT Bvarate models are useful when analyzng longtudnal data

More information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

Assembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface.

Assembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface. IDC Herzlya Shmon Schocken Assembler Shmon Schocken Sprng 2005 Elements of Computng Systems 1 Assembler (Ch. 6) Where we are at: Human Thought Abstract desgn Chapters 9, 12 abstract nterface H.L. Language

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

Reading. 14. Subdivision curves. Recommended:

Reading. 14. Subdivision curves. Recommended: eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton

More information

NGPM -- A NSGA-II Program in Matlab

NGPM -- A NSGA-II Program in Matlab Verson 1.4 LIN Song Aerospace Structural Dynamcs Research Laboratory College of Astronautcs, Northwestern Polytechncal Unversty, Chna Emal: lsssswc@163.com 2011-07-26 Contents Contents... 1. Introducton...

More information

A DATA ANALYSIS CODE FOR MCNP MESH AND STANDARD TALLIES

A DATA ANALYSIS CODE FOR MCNP MESH AND STANDARD TALLIES Supercomputng n uclear Applcatons (M&C + SA 007) Monterey, Calforna, Aprl 15-19, 007, on CD-ROM, Amercan uclear Socety, LaGrange Par, IL (007) A DATA AALYSIS CODE FOR MCP MESH AD STADARD TALLIES Kenneth

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

Gaussian elimination. System of Linear Equations. Gaussian elimination. System of Linear Equations

Gaussian elimination. System of Linear Equations. Gaussian elimination. System of Linear Equations Jord Cortadella Department of Computer Scence Introducton to Programmng Dept. CS, UPC 2 An essental algorthm n Lnear Algebra wth multple applcatons: Solvng lnear systems of equatons Fndng the nverse of

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT 3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ

More information

Simulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010

Simulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010 Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement

More information