Fast algorithm for efficient simulation of quantum algorithm gates on classical computer
|
|
- Lenard Rodgers
- 5 years ago
- Views:
Transcription
1 Fast algort for effcet sulato of quatu algort gates o classcal coputer Sergey A PANFILOV Sergey V LYANOV Ludla V LITVINTSEVA Yaaa Motor Europe NV R&D Offce Va Braate Crea (CR) Italy Alexader V YAZENIN Dept of Iforatcs Tver State versty l Zelyabova Tver Russa Federato ABSTRACT Te geeral approac for quatu algort sulato o classcal coputer s troduced Effcet fast algort for sulato of Grover's quatu searc algort usorted database s preseted Coparso wt coo quatu algort sulato approac s deostrated Ts dvso perts to geeralze te approac of QA sulato ad to create a classcal tool to sulate ay type of kow QA Furter ore local optzato of QA copoets accordg to specfc ardware realzato akes t possble to develop approprate ardware accelerator of QA sulato usg classcal gates [3 4] Keywords: Quatu algort effcet sulato fast algorts INTRODCTION Quatu algorts (QA) deostrate great effcecy ay practcal tasks suc as factorzato of large teger ubers were classcal algorts are falg or draatcally effectve [] Practcal applcato s stll away due to lack of te pyscal ardware pleetato of quatu coputers Te dfferece betwee classcal ad QAs s followg: proble solved by QA s coded te structure of te quatu operators Iput to QA ts case s always te sae Output of QA says wc proble was coded I soe sese you gve a fucto to QA to aalyze ad QA returs ts property as a aswer Forally te probles solved by QAs could be stated as follows: Iput Proble A fucto f:{0} {0} Fd a certa property of f Repeated k tes x> x> S S F INT Iput Superposto Etagleet Iterferece Output > (a) F D INPT STEP STEP STEP 3 OTPT bt bt bt bt M E A S R E M E N T ϕ0> ϕν > ϕν> Tus QA studes qualtatve propertes of te fuctos Te core of ay QA s a set of utary quatu operators or quatu gates I practcal represetato quatu gate s a utary atrx wt partcular structure Te sze of ts atrx grows expoetally wt te uber of puts akg t possble to sulate QAs wt ore ta puts [] o classcal coputer wt vo Neua arctecture I ts report we preset a practcal approac to sulate ost of kow QAs o classcal coputers We preset te results of te classcal effcet sulato of te Grover s quatu searc algort (QSA) as a becark of ts approac STRCTRE OF QA GATE SYSTEM DESIGN Te backgroud of QA sulato s a geeralzed represetato of QA as a set of sequetally appled saller quatu gates as t s preseted o te Fgure a Fro te structural pot of vew eac QA requres a partcular set of quatu gates but geerally eac partcular set ca be dvded to tree a subsets wt sae fucto for all QAs: Superposto operators Etagleet operators ad Iterferece operators (b) Fgure : a) Crcut represetato of QA; b) Quatu crcut of Grover s QSA Geeralzed approac QA sulato I geeral ay QA ca be represeted as a crcut of saller quatu gates as t s deostrated o te Fgure [3] Te crcut preseted te Fgure s dvded o fve geeral steps: Step : Iput Quatu state vector s set up to a tal value for ts cocrete algort For exaple put for Grover s QSA s a quatu state φ0 descrbed as a tesor product φ = a 00 = a 0 0 () were 0 = ; = ; deotes Kroecker tesor 0 product operato [] Suc a quatu state ca be preseted as t s sow o te Fgure a SYSTEMICS CYBERNETICS AND INFORMATICS VOLME - NMBER 3 63
2 Step 5: Output O ts step perfored easureet operato (extracto of te state wt axu probablty) ad followg terpretato of te result For exaple case of Grover s QSA requred dex s coded frst bts of te easured bass vector Steps of QAs are realzed by utary quatu operators Sulato of quatu operators s a key pot geeral QA sulato I order to accelerate QAs basc quatu operators ust be studed Fgure : Dyacs of Grover s QSA probablty apltudes of state vector o eac algort step Te coeffcets te Eq () are called probablty apltudes [3] Probablty apltudes ay take egatve or eve coplex values Te oly oe costrat o te values of te probablty apltudes s a = () Te actual probablty of te arbtrary quatu state a to be easured s calculated as a square of ts probablty apltude value p = a Step : Superposto Te state of te quatu state vector s trasfored te way tat probabltes are dstrbuted uforly aog all bass states Te result of te superposto step of Grover s QSA s preseted o te Fgure b probablty apltude represetato ad te Fgure 3b probablty represetato Step 3: Etagleet Probablty apltudes of te bass vector correspodg to te curret proble are flpped wle rest bass vectors left ucaged Etagleet s doe va cotrolled NOT operato Result of etagleet operato applcato to te state vector after superposto operato s sow o te Fgure c ad te Fgure 3c Note tat a etagleet operato does ot affect te probablty of state vector to be easured Actually etagleet prepares a state wc ca ot be represeted as a tesor product of spler state vectors For exaple cosder state φ preseted o te Fgure b ad state φ preseted o te Fgure c: ( ) ( )( ) ( ) ( + + ) ( ) φ = = = φ = = = As t was sow above descrbed state φ ca be preseted as tesor product of spler states wle state φ ca ot Step 4: Iterferece Probablty apltudes are verted about te average value As a result te probablty apltude of states arked by etagleet operato wll crease Result of terferece operator applcato s preseted o te Fgure a a probablty apltude represetato ad te Fgure 3a a probablty represetato Fgure 3: Dyacs of Grover s QSA probabltes of state vector o eac algort step Ma QA operators We cosder superposto etagleet ad terferece operators fro sulato vew pot I ts case superposto ad terferece ave ore coplcated structure ad dffer fro algort to algort Ad te we cosder etagleet operators sce tey ave slar structure for all QAs ad dffer oly by fucto beg aalyzed Superposto operators of QAs I geeral te superposto operator cossts of te cobato of te tesor products adaard operators wt detty operator I : 0 = I = 0 For ost QAs te superposto operator ca be expressed as Sp = S = S = = (3) were ad are te ubers of puts ad of outputs respectvely left sde power operato eas tesor power Operator S depedg o te algort ay be or adaard operator or detty operator I Nubers of outputs as well as structures of correspodg superposto ad terferece operators are preseted te Table for dfferet QAs Note tat superposto ad terferece operators are ofte cota tesor power of adaard operator ( ) wc s called Wals-adaard operator ( W ) It s kow [3] tat eleets of te Wals-adaard operator could be obtaed as ( ) * (4) were = 0 = 0 + = / 64 SYSTEMICS CYBERNETICS AND INFORMATICS VOLME - NMBER 3
3 Table : Paraeters of superposto ad terferece operators of a quatu algorts Algort Superposto Iterferece Deutsc s I Deutsc- Jozsa s I Grover s D I So s I I Sor s I QFT I Ts approac proves greatly speedup of classcal sulato of te Wals adaard operators sce ts eleets could be obtaed by te sple replcato accordg to te rule preseted Eq (4) Exaple : Cosder superposto operator of Deutsc s algort = = S = I : ( ) * Deutsc [ Sp] = I / (5) 0*0 0* ( ) I ( ) I I I = *0 * = ( ) I ( ) I I I Exaple : Cosder superposto operator of Deutsc-Jozsa s ad of Grover s algort for te case = = S = : ( ) * Deutsc Jozsa ' s Grover ' s [ Sp] = / 0*0 0*0 0* 0* ( ) ( ) ( ) ( ) 0*0 0*0 0* 0* ( ) ( ) ( ) ( ) = (6) *0 * * * ( ) ( ) ( ) ( ) *0 * * * ( ) ( ) ( ) ( ) = Exaple 3: Superposto operator of So s ad of Sor s algorts = = S = I : ( ) * So Sor [ Sp] = I = / I I I I I I I I = I I I I I I I I Iterferece operators of a QAs Iterferece operators ust be selected for eac algort dvdually accordg to te paraeters preseted te Table Cosder soe partcular parts of terferece operators Iterferece operator cossts of terferece part wc s dfferet for all algorts ad fro easureet part wc s te sae for ost of algorts ad cossts of tesor power of detty operator Cosder terferece operator of eac algort Iterferece operator of Deutsc algort Iterferece operator of Deutsc s algort cossts of tesor product of two adaard trasforatos ad ca be calculated usg Eq (4) wt = : * Deutsc ( ) It = = = (7) / Note tat Deutsc s algort Wals-adaard trasforato terferece operator s used also for te easureet bass Iterferece operator of Deutsc-Jozsa s algort Iterferece operator of Deutsc-Jozsa s algort cossts of tesor product of power of Wals-adaard operator wt a detty operator I geeral for te block atrx of te terferece operator of Deutsc-Jozsa s algort ca be wrtte as: * Deutsc Jozsa ' s ( ) It = I (8) were = 0 = 0 Exaple 4: Iterferece operator of Deutsc-Jozsa s algort = = : * Deutsc Jozsa ' s ( ) It = I I I I I (9) I I I I = I I I I I I I I Iterferece operator of Grover s algort Iterferece operator of Grover s algort ca be wrtte as a block atrx of te followg for: Grover It = D I = I I / (0) I = = + I I = I / / / = were = 0 = 0 D refers to dffuso operator: AND( = ) ( ) [ D ] = / Exaple 5: Iterferece operator of Grover s QSA = = : SYSTEMICS CYBERNETICS AND INFORMATICS VOLME - NMBER 3 65
4 = = Grover It D I I I / = + I I = I I I I I I I I = I I I I I I I I () Wt = we ca observe te followg relato: QFT e e = = = J*(0*0) π / J*(0*) π / k k = J*(*0) π / J*(*) π / e e (4) Eq (3) ca be also preseted aroc for usg Euler forula: Note tat wt growg uber of qubts ga coeffcet wll becoe saller Deso of te atrx creases accordg to but eac eleet ca be extracted usg Eq (0) wtout allocato of etre operator atrx Iterferece operator of So s algort Iterferece operator of So s algort s prepared te sae aer as superposto (as well as superposto operators of Sor s algort) ad ca be descrbed as followg Eq () ad Eq(a): * So ( ) It = I = I / () 0*0 0* 0* ( ) ( ) I ( ) I ( ) I *0 * * ( = ) / ( ) I ( ) I ( ) I ( )*0 ( )* ( )*( ) ( ) I ( ) I ( ) I Reark I geeral terferece operator of So s algort cocdes wt terferece operator of Deutsc-Jozsa s algort Eq (8) but eac block of te operator atrx Eq () cossts of tesor products of detty operator Reark Eac odd block (we product of te dexes s a odd uber) of te So s terferece operator Eq () as a egatve sg Actually f = 0 4 or = 0 4 te block sg s postve else block sg s egatve Ts rule s applcable also for Eq (8) of Deutsc- Jozsa s algort terferece operator Te t s coveet to ceck f oe of te dexes s a eve uber stead of calculatg ter product Te Eq () ca be reduced as: ( ) * So / It = I = I = If s odd or f s odd I / f s eve ad s eve (a) Iterferece operator of Sor s algort Iterferece operator of Sor s algort uses Quatu Fourer Trasforato operator (QFT) [] calculated as: [ QFT ] π J(* ) = e (3) / were: J - agary ut = 0 ad = 0 π π QFT k cos ( * ) s ( * ) = J k/ k + k (5) Etagleet operators of a QAs I geeral etagleet operators are part of QA were te forato about te fucto beg aalyzed s coded as put-output relato Let s dscuss te geeral approac for codg bary fuctos to correspodg etagleet gates Cosder arbtrary bary fucto: f :{ 0} { 0 } suc tat: f( x0 x ) = ( y0 y ) I order to create utary quatu operator wc perfors te sae trasforato frst we trasfer rreversble fucto f to reversble fucto F as followg: + + F :{ 0} { 0 } suc tat: F( x0 x y0 y ) = = ( x x f( x x ) ( y y )) were deotes addto odulo avg reversble fucto F we ca desg a etagleet operator atrx usg te followg rule: B B [ F ] B B F = ff ( ) = 00;; + + B deotes bary codg Actually resulted etagleet operator s a block dagoal atrx of te for: M 0 0 F = (6) 0 M Eac block M = 0 cossts of tesor products of I or of C operators ad ca be obtaed as followg: Iff F( k) = 0 M = (7) k = 0 Cff F( k) = were C stays for NOT operator defed as: 0 C = 0 66 SYSTEMICS CYBERNETICS AND INFORMATICS VOLME - NMBER 3
5 It s clear tat etagleet operator s a sparse atrx sg property of sparse atrx operatos t s possble to accelerate te sulato of te etagleet Exaple 6: Etagleet operator for bary fucto: { } { } f : 0 0 suc tat: f( x ) = 0 x= 0 x 0 Reversble fucto F ts case wll be: 3 3 F :{ 0} { 0} suc tat: ( x y) ( x f( x) y) = = = 0 0 = = = = 0 0 = Te correspodg etagleet block atrx ca be wrtte as: I F = 0 0 C I I Fgure c deostrates te result of te applcato of ts operator Grover s QSA Etagleet operators of Deutsc ad of Deutsc-Jozsa s algorts ave te sae for Exaple 7: Etagleet operator for bary fucto: f :{ 0} { 0} suc tat: f( x ) = 0 00 x= 0 x I I F = 0 0 C I I I C I Etagleet operators of Sor ad of So s algorts ave te sae for 3 RESLTS OF CLASSICAL QA GATE SIMLATION Aalyzg quatu operators preseted te secto we ca do te followg splfcato for creasg perforace of classcal QA sulatos: a) All quatu operators are syetrcal aroud a dagoal atrces; b) State vector s allocated as a sparse atrx; c) Eleets of te quatu operators are ot stored but calculated we ecessary usg Eqs (6) (0) (6) ad (7); d) As terato codto we cosder u of Sao etropy of te quatu state calculated as: + S = p log p (8) = 0 Calculato of te Sao etropy s appled to te quatu state after terferece operato [5] Mu of Sao etropy Eq (8) correspods to te state we tere are few state vectors wt g probablty (states wt u ucertaty) Selecto of approprate terato codto s portat sce QAs are perodcal Fgure 4 sows results of te Sao forato etropy calculato for te Grover s algort wt 5 puts Iterato Fgure 4: Sao etropy aalyss of Grover s QSA dyacs wt fve puts Fgure 4 sows tat for fve puts of Grover s QSA a optal uber of teratos accordg to u of te Sao etropy crtera for successful result s exactly four After tat probablty of correct aswer wll decrease ad algort ay fal to produce correct aswer Note tat teoretcal estato π 5 for 5 puts gves = 444 teratos 4 Sulato results of fast Grover QSA are suarzed Table Nubers of teratos for fast algort were estated accordg to terato codto as u of Sao etropy of quatu state vector Te followg approaces were used sulato: Approac : Quatu operators are appled as atrces eleets of quatu operator atrces are calculated dyacally accordg to Eqs (6) (0) ad (7) Classcal ardware lt of ts approac s aroud 0 qubts caused by expoetal teporal coplexty Approac : Quatu operators are replaced wt classcal gates Product operatos are reoved fro sulato accordg to [4] State vector of probablty apltudes s stored copressed for (oly dfferet probablty apltudes are allocated eory) Wt secod approac t s possble to perfor classcal effcet sulato of Grover s QSA wt arbtrary large uber of puts (50 qubts ad ore) Wt allocato of te state vector coputer eory ts approac perts to sulato 6 qubts o PC wt GB of RAM Fgure 5 sows eory requred for Grover algort sulato we wole state vector s allocated eory Addg oe qubt requre double of te coputer eory eeded for sulato of Grover's QSA case we state vector s allocated copletely eory SYSTEMICS CYBERNETICS AND INFORMATICS VOLME - NMBER 3 67
6 Table : Teporal coplexty of Grover s QSA sulato o Gz coputer wt two CPs Nuber of teratos Approac (oe terato) Teporal coplexty secods Approac ( teratos) ~ ~ ~ ~ ~ ~ approaces [] gve us ew effectve possblty for sulato of quatu cotrol algorts usg classcal coputers Fgure 6: Teporal coplexty of Grover's QSA 5 REFERENCES Fgure 5: Spatal coplexty of Grover QA sulato Teporal coplexty of Grover's QSA s preseted Fgure 6 I ts case state vector s allocated eory ad quatu operators are replaced wt classcal gates accordg to [3 4] Fastest case s we we copress state vector ad replace quatu operator atrces wt correspodg classcal gates accordg wt [34] I ts case we obta speedup accordg to Approac 4 CONCLSIONS Effcet sulato of QAs o classcal coputer wt large uber of puts s dffcult proble For exaple to operate oly wt 50 qubts state vector drectly t s ecessary to ave at least 8TB of eory (for te oet largest supercoputer as oly 0TB [6]) I preset report for cocrete portat exaple as Grover s QSA [] t s deostrated te possblty to overrde spato-teporal coplexty ad to perfor effcet sulatos of QA o classcal coputers Coparso wt sparse atrx based [] P Sor Wy ave't ore quatu algorts bee foud? Joural of te ACM (JACM) Vol 50 Issue pp ; L G Valat Quatu crcuts tat ca be sulated classcally polyoal te SIAM J of Coputg Vol 3 No 4 pp ; L Grover Quatu ecacs elps searcg of te eedle a aystack Pys Rev Lett Vol 79 No pp ; M Nelse ad I Cuag Quatu Coputato ad Quatu Iforato Cabrdge v Press 00 [] J Nwa K Matsuoto ad Ia Geeral-purpose parallel sulator for quatu coputg Pys Rev A Vol [3] SV lyaov F Gs S Paflov I Kurawak ad LV Ltvtseva Sulato of quatu algorts o classcal coputers verstà degl Stud d Mlao Polo Ddattco e d Rcerca d Crea Note del Polo Vol 3 Crea 999 [4] P Aato S lyaov D Porto SA Paflov ad G Rzzotto ardware arctecture syste desg of quatu algort gates for effcet sulato o classcal coputers Proc SCI 003 Vol 3 pp Orlado 003 [5] SV lyaov SA Paflov I Kurawak AV Yaze Iforato aalyss of quatu gates for sulato of quatu algorts o classcal coputers Proc QCM&C000 Kluwer Acadec/Pleu Publ pp [6] ttp://wwwesastecgop 68 SYSTEMICS CYBERNETICS AND INFORMATICS VOLME - NMBER 3
KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.
KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces
More informationSome results and conjectures about recurrence relations for certain sequences of binomial sums.
Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have
More informationSome Different Perspectives on Linear Least Squares
Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,
More information( t) ( t) ( t) ρ ψ ψ. (9.1)
Adre Toaoff, MT Departet of Cestry, 3/19/29 p. 9-1 9. THE DENSTY MATRX Te desty atrx or desty operator s a alterate represetato of te state of a quatu syste for wc we ave prevously used te wavefucto. Altoug
More informationA New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming
ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationAlgorithms behind the Correlation Setting Window
Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree
More informationPRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION
PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of
More informationGeneral Method for Calculating Chemical Equilibrium Composition
AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt
More information2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission
/0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power
More informationCoherent Potential Approximation
Coheret Potetal Approxato Noveber 29, 2009 Gree-fucto atrces the TB forals I the tght bdg TB pcture the atrx of a Haltoa H s the for H = { H j}, where H j = δ j ε + γ j. 2 Sgle ad double uderles deote
More informationStationary states of atoms and molecules
Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal
More information1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.
CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy
More informationLecture 8 IEEE DCF Performance
Lecture 8 IEEE82. DCF Perforace IEEE82. DCF Basc Access Mechas A stato wth a ew packet to trast otors the chael actvty. If the chael s dle for a perod of te equal to a dstrbuted terfrae space (DIFS), the
More informationBasic Concepts in Numerical Analysis November 6, 2017
Basc Cocepts Nuercal Aalyss Noveber 6, 7 Basc Cocepts Nuercal Aalyss Larry Caretto Mecacal Egeerg 5AB Sear Egeerg Aalyss Noveber 6, 7 Outle Revew last class Mdter Exa Noveber 5 covers ateral o deretal
More informationA Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming
Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter
More informationParallelized methods for solving polynomial equations
IOSR Joural of Matheatcs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X. Volue 2, Issue 4 Ver. II (Jul. - Aug.206), PP 75-79 www.osrourals.org Paralleled ethods for solvg polyoal equatos Rela Kapçu, Fatr
More informationSolving the fuzzy shortest path problem on networks by a new algorithm
Proceedgs of the 0th WSEAS Iteratoal Coferece o FUZZY SYSTEMS Solvg the fuzzy shortest path proble o etworks by a ew algorth SADOAH EBRAHIMNEJAD a, ad REZA TAVAKOI-MOGHADDAM b a Departet of Idustral Egeerg,
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More informationConstruction of Composite Indices in Presence of Outliers
Costructo of Coposte dces Presece of Outlers SK Mshra Dept. of Ecoocs North-Easter Hll Uversty Shllog (da). troducto: Oftetes we requre costructg coposte dces by a lear cobato of a uber of dcator varables.
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationOn the Capacity of Bounded Rank Modulation for Flash Memories
O the Capacty of Bouded Rak Modulato for Flash Meores Zhyg Wag Electrcal Egeerg Departet Calfora Isttute of Techology Pasadea, CA 95, USA Eal: zhyg@paradsecaltechedu Axao (Adrew) Jag Coputer Scece Departet
More informationAsymptotic Formulas Composite Numbers II
Iteratoal Matematcal Forum, Vol. 8, 203, o. 34, 65-662 HIKARI Ltd, www.m-kar.com ttp://d.do.org/0.2988/mf.203.3854 Asymptotc Formulas Composte Numbers II Rafael Jakmczuk Dvsó Matemátca, Uversdad Nacoal
More informationSolutions to problem set ); (, ) (
Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More information( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.
Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof
More informationOn the Capacity of Bounded Rank Modulation for Flash Memories
O the Capacty of Bouded Rak Modulato for Flash Meores Zhyg Wag Electrcal Egeerg Departet Calfora Isttute of Techology Pasadea, CA 925, USA Eal: zhyg@paradsecaltechedu Axao (Adrew) Jag Coputer Scece Departet
More informationNon-degenerate Perturbation Theory
No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,
More informationStandard Deviation for PDG Mass Data
4 Dec 06 Stadard Devato for PDG Mass Data M. J. Gerusa Retred, 47 Clfde Road, Worghall, HP8 9JR, UK. gerusa@aol.co, phoe: +(44) 844 339754 Abstract Ths paper aalyses the data for the asses of eleetary
More informationA Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions
Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple
More informationInterval extension of Bézier curve
WSEAS TRANSACTIONS o SIGNAL ROCESSING Jucheg L Iterval exteso of Bézer curve JUNCHENG LI Departet of Matheatcs Hua Uversty of Huates Scece ad Techology Dxg Road Loud cty Hua rovce 47 R CHINA E-al: ljucheg8@6co
More informationAnalytical Study of Fractal Dimension Types in the Context of SPC Technical Paper. Noa Ruschin Rimini, Irad Ben-Gal and Oded Maimon
Aalytcal Study of Fractal Deso Types the Cotext of SPC Techcal Paper oa Rusch R, Irad Be-Gal ad Oded Mao Departet of Idustral Egeerg, Tel-Avv Uversty, Tel-Avv, Israel Ths paper provdes a aalytcal study
More informationGeneralization of the Dissimilarity Measure of Fuzzy Sets
Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationTransforms that are commonly used are separable
Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )
More informationSparse Gauss-Hermite Quadrature Filter For Spacecraft Attitude Estimation
Aerca Cotrol Coferece Marrott Waterfrot, Baltore, MD, USA Jue -July, ha5.4 Sparse Gauss-Herte Quadrature Flter For Spacecraft Atttude Estato B Ja, Mg, Yag Cheg Abstract I ths paper, a ew olear flter based
More informationA New Method for Decision Making Based on Soft Matrix Theory
Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer
More informationOn Probability of Undetected Error for Hamming Codes over Q-ary Symmetric Channel
Joural of Coucato ad Coputer 8 (2 259-263 O Probablty of Udetected Error for Hag Codes over Q-ary Syetrc Chael Mash Gupta, Jaskar Sgh Bhullar 2 ad O Parkash Vocha 3. D.A.V. College, Bathda 5, Ida 2. Malout
More informationROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K
ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu
More informationA Mean Deviation Based Method for Intuitionistic Fuzzy Multiple Attribute Decision Making
00 Iteratoal Coferece o Artfcal Itellgece ad Coputatoal Itellgece A Mea Devato Based Method for Itutostc Fuzzy Multple Attrbute Decso Makg Yeu Xu Busess School HoHa Uversty Nag, Jagsu 0098, P R Cha xuyeoh@63co
More informationThe Mathematics of Portfolio Theory
The Matheatcs of Portfolo Theory The rates of retur of stocks, ad are as follows Market odtos state / scearo) earsh Neutral ullsh Probablty 0. 0.5 0.3 % 5% 9% -3% 3% % 5% % -% Notato: R The retur of stock
More informationPolyphase Filters. Section 12.4 Porat
Polyphase Flters Secto.4 Porat .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful buldg
More informationA Bivariate Distribution with Conditional Gamma and its Multivariate Form
Joural of Moder Appled Statstcal Methods Volue 3 Issue Artcle 9-4 A Bvarate Dstrbuto wth Codtoal Gaa ad ts Multvarate For Sue Se Old Doo Uversty, sxse@odu.edu Raja Lachhae Texas A&M Uversty, raja.lachhae@tauk.edu
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationAN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET
AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET Abstract. The Permaet versus Determat problem s the followg: Gve a matrx X of determates over a feld of characterstc dfferet from
More informationD. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1
D. L. Brcker, 2002 Dept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/XD 2/0/2003 page Capactated Plat Locato Proble: Mze FY + C X subject to = = j= where Y = j= X D, j =, j X SY, =,... X 0, =,
More informationSUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi
Faculty of Sceces ad Matheatcs, Uversty of Nš, Serba Avalable at: http://wwwpfacyu/float Float 3:3 (009), 303 309 DOI:098/FIL0903303J SUBCLASS OF ARMONIC UNIVALENT FUNCTIONS ASSOCIATED WIT SALAGEAN DERIVATIVE
More informationQueueing Networks. γ 3
Queueg Networks Systes odeled by queueg etworks ca roughly be grouped to four categores. Ope etworks Custoers arrve fro outsde the syste are served ad the depart. Exaple: acket swtched data etwork. γ µ
More informationSymmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method
Syetry of the Soluto of Sedefte Progra by Usg Pral-Dual Iteror-Pot Method Yoshhro Kao Makoto Ohsak ad Naok Katoh Departet of Archtecture ad Archtectural Systes Kyoto Uversty Kyoto 66-85 Japa kao@s-jarchkyoto-uacjp
More informationSebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions
Sebastá Martí Ruz Alcatos of Saradache Fucto ad Pre ad Core Fuctos 0 C L f L otherwse are core ubers Aerca Research Press Rehoboth 00 Sebastá Martí Ruz Avda. De Regla 43 Choa 550 Cadz Sa Sarada@telele.es
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationNumerical Analysis Formulae Booklet
Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx
More informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationDecomposition of Hadamard Matrices
Chapter 7 Decomposto of Hadamard Matrces We hae see Chapter that Hadamard s orgal costructo of Hadamard matrces states that the Kroecer product of Hadamard matrces of orders m ad s a Hadamard matrx of
More informationReliability evaluation of distribution network based on improved non. sequential Monte Carlo method
3rd Iteratoal Coferece o Mecatrocs, Robotcs ad Automato (ICMRA 205) Relablty evaluato of dstrbuto etwork based o mproved o sequetal Mote Carlo metod Je Zu, a, Cao L, b, Aog Tag, c Scool of Automato, Wua
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationModule 7: Probability and Statistics
Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationRobust Mean-Conditional Value at Risk Portfolio Optimization
3 rd Coferece o Facal Matheatcs & Applcatos, 30,3 Jauary 203, Sea Uversty, Sea, Ira, pp. xxx-xxx Robust Mea-Codtoal Value at Rsk Portfolo Optato M. Salah *, F. Pr 2, F. Mehrdoust 2 Faculty of Matheatcal
More informationF. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties
進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationNonlinear Piecewise-Defined Difference Equations with Reciprocal Quadratic Terms
Joural of Matematcs ad Statstcs Orgal Researc Paper Nolear Pecewse-Defed Dfferece Equatos wt Recprocal Quadratc Terms Ramada Sabra ad Saleem Safq Al-Asab Departmet of Matematcs, Faculty of Scece, Jaza
More informationSEMI-TIED FULL-COVARIANCE MATRICES FOR HMMS
SEMI-TIED FULL-COVARIANCE MATRICES FOR HMMS M.J.F. Gales fg@eg.ca.ac.uk Deceber 9, 997 Cotets Bass. Block Dagoal Matrces : : : : : : : : : : : : : : : : : : : : : : : : : : : :. Cooly used atrx dervatve
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More information5. Data Compression. Review of Last Lecture. Outline of the Lecture. Course Overview. Basics of Information Theory: Markku Juntti
: Markku Jutt Overvew The deas of lossless data copresso ad source codg are troduced ad copresso lts are derved. Source The ateral s aly based o Sectos 5. 5.5 of the course book []. Teleco. Laboratory
More informationGlobal Optimization for Solving Linear Non-Quadratic Optimal Control Problems
Joural of Appled Matheatcs ad Physcs 06 4 859-869 http://wwwscrporg/joural/jap ISSN Ole: 37-4379 ISSN Prt: 37-435 Global Optzato for Solvg Lear No-Quadratc Optal Cotrol Probles Jghao Zhu Departet of Appled
More informationHigh Dynamic Range 3-Moduli Set with Efficient Reverse Converter
Hgh Dyac Rage 3-odul et wth Effcet Resdue to Bary Coverter Hgh Dyac Rage 3-odul et wth Effcet Reverse Coverter A. Harr, R. Rastegar, K. av Abstract-Resdue uber yste (R) s a valuable tool for fast ad parallel
More informationA Study on Generalized Generalized Quasi hyperbolic Kac Moody algebra QHGGH of rank 10
Global Joural of Mathematcal Sceces: Theory ad Practcal. ISSN 974-3 Volume 9, Number 3 (7), pp. 43-4 Iteratoal Research Publcato House http://www.rphouse.com A Study o Geeralzed Geeralzed Quas (9) hyperbolc
More information44 Chapter 3. Find the 13 term and the sum of the first 9 terms of the geometric sequence 48, 24, 12, 6, 3, 3 2 Solution 2
44 Chapter 3 Fd e 3 ter ad e su of e frst 9 ters of e geoetrc sequece 48, 24, 2, 6, 3, 3 2, á. We have a 48 ad r 2. Usg part (a) of Theore 3.2, we fd at e 3 ter s 48( 2 ) 3 2 256. Usg (3.4d), e su of e
More information3D Reconstruction from Image Pairs. Reconstruction from Multiple Views. Computing Scene Point from Two Matching Image Points
D Recostructo fro Iage ars Recostructo fro ultple Ves Dael Deetho Fd terest pots atch terest pots Copute fudaetal atr F Copute caera atrces ad fro F For each atchg age pots ad copute pot scee Coputg Scee
More informationare positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.
Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. August 2011
Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly
More informationA New Mathematical Approach for Solving the Equations of Harmonic Elimination PWM
New atheatcal pproach for Solvg the Equatos of Haroc Elato PW Roozbeh Nader Electrcal Egeerg Departet, Ira Uversty of Scece ad Techology Tehra, Tehra, Ira ad bdolreza Rahat Electrcal Egeerg Departet, Ira
More informationPseudo-random Functions
Pseudo-radom Fuctos Debdeep Mukhopadhyay IIT Kharagpur We have see the costructo of PRG (pseudo-radom geerators) beg costructed from ay oe-way fuctos. Now we shall cosder a related cocept: Pseudo-radom
More informationDescriptive Statistics
Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people
More informationCS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht los@cs.ptt.edu 59 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear cobato of put copoets f + + + K d d K k - paraeters eghts
More information3.1 Introduction to Multinomial Logit and Probit
ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore
More informationTaylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific
CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres
More informationThe internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test
Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More informationDistributed Fusion Filter for Asynchronous Multi-rate Multi-sensor Non-uniform Sampling Systems
Dstrbuted Fuso Flter for Asychroous Mult-rate Mult-sesor No-ufor Saplg Systes Jg Ma, Hogle L School of Matheatcs scece Helogjag Uversty Harb, Helogjag Provce, Cha ajg47@gal.co, lhogle8@63.co Shul Su Departet
More informationJournal of Emerging Trends in Computing and Information Sciences
Joural of Eergg Treds Coputg ad Iforato Sceces 2009-2013 CIS Joural. All rghts reserved. http://www.csjoural.org A Detaled Study o the Modul Nuber Effect o RNS Tg Perforace 1 Da Youes, 2 Pavel Steffa 1
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationCAS Wavelet Function Method for Solving Abel Equations with Error Analysis
It J Res Id Eg Vol 6 No 4 7 3 364 Iteratoal Joural of Research Idustral Egeerg wwwrejouralco CAS Wavelet Fucto ethod for Solvg Abel Equatos wth Error Aalyss E Fathzadeh R Ezzat K aleejad Departet of atheatcs
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationDATA DOMAIN DATA DOMAIN
3//6 Coprght otce: Most ages these sldes are Gozalez ad oods Pretce-Hall Note: ages are [spatall] ostatoar sgals. e eed tools to aalze the locall at dfferet resolutos e ca do ths the data doa or sutable
More informationA Characterization of Jacobson Radical in Γ-Banach Algebras
Advaces Pure Matheatcs 43-48 http://dxdoorg/436/ap66 Publshed Ole Noveber (http://wwwscrporg/joural/ap) A Characterzato of Jacobso Radcal Γ-Baach Algebras Nlash Goswa Departet of Matheatcs Gauhat Uversty
More informationTHE TRUNCATED RANDIĆ-TYPE INDICES
Kragujeac J Sc 3 (00 47-5 UDC 547:54 THE TUNCATED ANDIĆ-TYPE INDICES odjtaba horba, a ohaad Al Hossezadeh, b Ia uta c a Departet of atheatcs, Faculty of Scece, Shahd ajae Teacher Trag Uersty, Tehra, 785-3,
More informationInternational Journal of Mathematical Archive-3(5), 2012, Available online through ISSN
Iteratoal Joural of Matheatcal Archve-(5,, 88-845 Avalable ole through www.a.fo ISSN 9 546 FULLY FUZZY LINEAR PROGRAMS WITH TRIANGULAR FUZZY NUMERS S. Mohaaselv Departet of Matheatcs, SRM Uversty, Kattaulathur,
More informationRobust Mean-Conditional Value at Risk Portfolio Optimization
Iteratoal Joural of Ecooc Sceces Vol. III / No. / 204 Robust Mea-Codtoal Value at Rsk Portfolo Optzato Farzaeh Pr, Mazar Salah, Farshd Mehrdoust ABSRAC I the portfolo optzato, the goal s to dstrbute the
More informationOrder Nonlinear Vector Differential Equations
It. Joural of Math. Aalyss Vol. 3 9 o. 3 39-56 Coverget Power Seres Solutos of Hgher Order Nolear Vector Dfferetal Equatos I. E. Kougas Departet of Telecoucato Systes ad Networs Techologcal Educatoal Isttute
More informationUniform DFT Filter Banks 1/27
.. Ufor FT Flter Baks /27 Ufor FT Flter Baks We ll look at 5 versos of FT-based flter baks all but the last two have serous ltatos ad are t practcal. But they gve a ce trasto to the last two versos whch
More informationOn Hilbert Kunz Functions of Some Hypersurfaces
JOURNAL OF ALGEBRA 199, 499527 1998 ARTICLE NO. JA977206 O HlbertKuz Fuctos of Soe Hypersurfaces L Chag* Departet of Matheatcs, Natoal Tawa Uersty, Tape, Tawa ad Yu-Chg Hug Departet of Matheatcs, Natoal
More informationDepartment of Mechanical Engineering ME 322 Mechanical Engineering Thermodynamics. Ideal Gas Mixtures. Lecture 31
Departet of echacal Egeerg E 322 echacal Egeerg Therodyacs Ideal Gas xtures Lecture 31 xtures Egeerg Applcatos atural gas ethae, ethae, propae, butae, troge, hydroge, carbo doxde, ad others Refrgerats
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More information