SlideShare a Scribd company logo
Spectral methods for linear
systems with random inputs
   A parameterized matrix view
         David F. Gleich
         Sandia National Laboratories
         with Paul Constantine @ Sandia
         and Gianluca Iaccarino @ Stanford
Spectral methods for linear
systems with random inputs
   A parameterized matrix view
        First     linear systems
        Second    random inputs
        Third     parameterized
                  matrices
        Fourth    spectral methods
David F. Gleich (Sandia)          Parameterized Matrices   3 / 38



   Computational Science
     Discretizing Reality

                           Start with physical model
                           Discretize space and time
                           Arrive at linear system or
                           eigenvalue problem
David F. Gleich (Sandia)   Parameterized Matrices   4 / 38



   Computational Science
     Discretizing Reality
David F. Gleich (Sandia)    Parameterized Matrices   5 / 38



   Computational Science
     Discretizing Reality



                           Ax = b
David F. Gleich (Sandia)        Parameterized Matrices   6 / 38



   Matrices at this workshop


                                      A
                           Random Gaussian
                           Random sums of
                           independent matrices
                           Random adjacency
                           matrices
David F. Gleich (Sandia)   Parameterized Matrices                          7 / 38




                              Fireflies and Jellybeans, Creative Commons


                              ∇·∇ =ƒ
David F. Gleich (Sandia)          Parameterized Matrices                          8 / 38




                                     Fireflies and Jellybeans, Creative Commons

                              ∇ · (α(s, )∇ ) = ƒ
                           K + s1 K1 + s2 K2 + . . . = f
David F. Gleich (Sandia)                    Parameterized Matrices                   9 / 38



   My favorite model PAG E R A N K
                           3
                                                  1. follow out-edges uniformly with
                                                     probability α, and
                  2                    5

                           4
                                                  2. randomly jump according to v
                                                     with probability 1 − α, we’ll as-
                  1                    6
                                                     sume    = 1/ n.

         1/ 6             ↓                   Induces a Markov chain model
                  1/ 2    0      0     0   0
           1/ 6    0      0     1/ 3   0   0       αP + (1 − α)veT x(α) = x(α)
         1/ 6    1/ 2    0     1/ 3   0   0
         1/ 6     0     1/ 2    0     0   0
           1/ 6    0     1/ 2   1/ 3   0   1  or the linear system
           1/ 6    0      0      0     1   0
                                                         ( − αP)x(α) = (1 − α)v
                           P
David F. Gleich (Sandia)                      Parameterized Matrices               10 / 38



   The PageRank Random Variable
                       3.0         InfBeta( 3.2 , 2.0 , 1.9e−05 , 0.0019 )
                       2.5

                       2.0
             density




                       1.5

                       1.0

                       0.5

                       0.0
                             0.0        0.2          0.4         0.6   0.8   1.0
                                                      Raw α
David F. Gleich (Sandia)   Parameterized Matrices   11 / 38



   Parameterized Matrices
      Better Discretized Reality


               A(s)x(s) = b(s)
David F. Gleich (Sandia)       Parameterized Matrices   12 / 38



   Parameterized Matrices
      Better Discretized Reality


               A(s)x(s) = b(s)
        s   - independent random variables/parameters
                      bounded, analytic, non-singular
David F. Gleich (Sandia)       Parameterized Matrices                               13 / 38



   A Parameterized Matrix View of
      Uncertainty Quantification
   Setup
       A(s)x(s) = b(s)
               s∈D

           ƒ =          ƒ ds
                    D
                                        Wi l l m y c o o ki e s b u rn ?
   Questions
       E[x(s)] = 〈x(s)〉
            Std[x(s)]
        P { (s) ≥ γ}
      x(s) ≈ faster y(s)                       Fireflies and Jellybeans, Creative Commons
David F. Gleich (Sandia)      Parameterized Matrices   14 / 38



   Uncertainty Quantification
      At this workshop
             Richmond
                 Unknown sensor array locations.
             Schehr
                 Where are the viscious walkers?
             Antonsen
                 Uncertain component structure.

                       Assumed "totally" random
David F. Gleich (Sandia)                       Parameterized Matrices                                                       15 / 38



   A new type of sensitivity analysis
          Ulam Networks on the Chirikov Map
          Chirikov map                         Ulam network
          yt+1 = ηyt +k sin(             t +θt ) 1. divide phase space into uniform cells
           t+1 = t + yt+1                      2. form P based on trajectories.




                        log(E [x(A)])                               log(Std [x(A)]))/ log(E [x(A)])
                                                 A ∼ Bet (2, 16)
          Note    White is larger, black is smaller
                                Google matrix, dynamical attractors, and Ulam networks, Shepelyansky and Zhirov, arXiv
                 David F. Gleich (UBC)                  Random sensitivity                               Sandia   23 / 37
David F. Gleich (Sandia)                         Parameterized Matrices                                               16 / 38



   Improved web-spam classification
          Webspam application


              Hosts of uk-2006 are labeled as spam, not-spam, other

                                         P           R                        f       FP        FN
            Baseline                     0.694       0.558                    0.618   0.034     0.442

            Beta(0.5,1.5)                0.695       0.561                    0.621   0.034     0.439
            Beta(1,1)                    0.698       0.562                    0.622   0.033     0.438
            Beta(2,16)                   0.699       0.562                    0.623   0.033     0.438



          Note Bagged (10) J48 decision tree classifier in Weka, mean of 50 repetitions from
          10-fold cross-validation of 4948 non-spam and 674 spam hosts (5622 total).
                                                   Becchetti et al. Link analysis for Web spam detection, 2008.
                 David F. Gleich (UBC)                   Random sensitivity                        Sandia   29 / 37
David F. Gleich (Sandia)         Parameterized Matrices   17 / 38



   Solutions are rational or analytic

                               A(s)x(s) = b(s)

                                        det(A (s))
                                (s) =
                                        det(A(s))

                           A = A(s) with ith column
                              replaced by b(s)
David F. Gleich (Sandia)                                                                                                 Parameterized Matrices                                                                                                                                                18 / 38



   A viable computational strategy?
                ⋅                                                                                                                                                                                                                                              . ⋅




                                                  f (α) = 1724683103168320512000α 102 − 351689859974563275916800α 101 + 1046657678560756011923040α 100    (α) = 21252680112847680000α 102
                                          +332821515558986503317268308α 99 + 202994690094545539249274953458α 98 + 701216550622104187641429941160α 97     −3542775096896042918400α 101 − 377301357230918051819160α 100 + 62030166204003769204027938α 99 + 301903572553392042618587937α 98
                           +38942435173273232195508862504752α 96 − 5204876256969489587508598423780757α 95 − 53419116345848724180375395029139614α 94      −27515144995670593102754792187α 97 − 1391342388530090922919905979557α 96 − 11397010225845179645798293856049α 95
                                                             +1621997105501543781796265745838677670α 93 + 17992097277595516775992937444966323725α 92     +487046819801240647260974920877667α 94 + 8641748415645906110710596472701695α 93 − 14615573868254463557271968794871527α 92
                                                        −228388738389199148614341585444680228464α 91 − 2572935401339464873388154472765864295466α 90      −1455304405730842808585234463006780870α 91 − 16140532952116322684344866986683755014α 90
                                                    −18662047188535851000868073690251020472621α 89 − 155192964832717622674637679380949267008397α 88      −107685923577790689207116358432796101348α 89 + 3574857500140390342079726927167132783327α 88
                                             +13633798075806927018912795365187923947976816α 87 + 153692481592717017931843564092779914769739855α 86       +76245995916566900197088870723441134067760α 87 − 320477613697118756563592647774688786780579α 86
                                         −2424702525231324896856434133527720085459106818α 85 − 34112664906875644324640001664890877920583430935α 84       −14315018719450474212530996756919665488506623α 85 − 12271042346558183829899943919127664848771235α 84
                                     +222921632950502905446093540571509314548545319158α 83 + 4458381340774458139955262362762709170337141183042α 82       +1538719934896052457300693234469902122130588440α 83 + 7259823837632938466306787148779956756499503259α 82
                                  −9722398912749159172830586061232227612575398195577α 81 − 402863595222192101330043246404750577170418624210463α 80       −91383277962053778179963631846131934198363974003α 81 − 912158632690159715631486922494993985581191177254α 80
                              −241296146875962767748365749082981265577900593669099α 79 + 26884891161116233003550134767867058390000240645389885α 78       +1124589169570249225316595386438810701468062018941α 79 − 55599491760340084897708205765116975153096053881206α 78
                          +75002935639704657680175868562515328344632861061620026α 77 − 1355245718493528694128677343628002432897202221776993666α 76       +254197028878341726795811304127085084201803714274594α 77 − 1155102780712932745491921904562487673324953687625090α 76
                       −6666337432948865424681896342751813538288258918631143898α 75 + 50876562123828411130342908134923596879946044492587906688α 74       −19623309116424352882311523132748440745863270150867432α 75 − 72367264828688457023192884699324797029606326773402260α 74
                   +385972738637461890892793659070699381929652086327544953064α 73 − 1324370012053495348856190918458325441254102678707139546912α 72       +510591330662979105902331311824358111451756310585317896α 73 + 6560635654785580651459993551515346226540950556472012168α 72
                −16416792980158036153780188009203628703318521649963318398744α 71 + 17510197624369310054645143199845105805941154913191274775360α 70       +11841946546859350197679256661965428675545845230913012752α 71 − 222422692257166102165445803087102201095333519552710152624α 70
              +533320137070985354296793454864336229974212018883255863520736α 69 + 275502212308122569075672900514808641788656066608417565862128α 68       −1447290325427425453794609658098719385231428839474861685840α 69 + 2125011726240928873652963898522501443619028980101705108896α 68
                                                                               −13429082722840051523544458153489421210623008268881676515202688α 67       +56163879158282775333105949842095267377034088228166264755488α 67 + 133653341840138472687713523321901358136789047544268798190144α 66
                                                                               −23110058843365910555627839838104471746030299594537756688223008α 66       −1165851790876533575106055126719543401792990924852555883239232α 65
                                                                              +262081257818502675810469542460738736851208401216965512926700160α 65       −7205045167922126127366881708591461911830986630512778219907200α 64
                                                                              +729407390179003876249104385055674850942454472967192021090685376α 64       +8196149623293434725419276185048399130126199483584663609965696α 63
                                                                             −3847937179452929633833233710422322341537775007885518269634539392α 63       +190347290617372900092754118891814664663338859287254054095265536α 62
                                                                            −15488141989129507247130473020571135237573107436265881323677072000α 62       +296403177926940870392191966640325276665391672647048523475737600α 61
                                                                            +36050325771659567239591241663693950811960305821938730156334667776α 61       −3179986962227253427695124755087565566711837258936975824737021952α 60
                                                                           +246707867322513330007744656494007568641366676837744833157870986240α 60       −12273950891286672757637149571293897139589064857886165164957404160α 59
                                                                            +66698815198854350338382524697115939758820557665663603703007667712α 59       +31408962973625270006925545397999409094566386715881351869322999808α 58
                                                                          −2959446110396107328472639479854607457433633185566140760490226286592α 58       +253177395609699067378776631302481890469651122338031051366108686336α 57
                                                                         −12528512804728910558071029225789548204605758683928995029146000314368α 57       −15354832074031738521204442047058295183786064138590507845987942400α 56
                                                                         +19985525277247932558760938212461479524515746377831707793868714172416α 56       −3457076532174502560822426326142749948730584183953208907119801098240α 55
                                                                        +343866190600408921247069416527135879796528858737524668958998645633024α 55       −6661437625275114934838338879511817915494254490727882100057772130304α 54
                                                                        +237159992339459130849980507259488489676582642639199883151854812422144α 54       +28704083600179676384022705580143799967745682382583318411010759639040α 53
                                                                       −6150352682504179603648657901968989091083378789857325448622418220859392α 53
                                                                                                                                                         +173119877625293135511416194747967318688771201702803231109775079243776α 52
                                                                      −12507084588874068660420542622454441021005365876210831205762085535989760α 52
                                                                                                                                                         +42285615967170654345485778244291908234053330314299949447131636826112α 51
                                                                      +76052343558405304817491728967709919562879906814237879556140479278219264α 51
                                                                                                                                                         −3092545165791022831669116892040565590342926023532342815170675350831104α 50
                                                                     +281657470545819893901842735393494111347269819443029672934492155921629184α 50
                                                                                                                                                         −7385454932946443098573906964601689327710122151758555775183630113177600α 49
                                                                     −524010169549932716315240835391286383538294517356494888193446880264060928α 49
                                                                                                                                                         +44090325705050939960465955316629060665099648652920301218388343039721472α 48
                                                                    −4283228548253488673520351046009849054273946705738400536855052450584985600α 48
                                                                                                                                                         +180430494757250498411208705426475191214202221095549279916495110854934528α 47
                                                                    −2155194129185085332436034710334032595487897368550943059587873095183237120α 47



                                 ƒ (s)
                                                                                                                                                         −493525709032718650057526281767644848135900953167613963100373354560880640α 46
                                                                   +44942983365390912258646063248936155917171235534162037124027584790839951360α 46
                                                                  +123764976043225311633569878034493895722302903722502785220748272524591104000α 45       −3036843091999605016958964058463815080108229170215714733277797291506270208α 45


                                                                                                                                                                                                                                                             g(s)
                                                                  −263604612819883334094471942440378055857630908721587551326277602165812887552α 44       +3865732160987803528525842299699004166440912343665407865787648656852123648α 44
                                                                 −2043045823645899057845901056050369454115577248500633141166053687383937777664α 43       +40165478772124194334610082404062794103423683134161618111009172215000203264α 43
                                                                  −883572534249006235663814128436259426227447113734226469390794110452279279616α 42       −11270446090439842262616868429380066718469755470664378191173671836048162816α 42
                                                                +22029266389692672474905374638580604237511322238870051881693348503640495620096α 41       −431725269187383778295706776607285692623377582173153891079752971949306806272α 41
                                                                +45203159614332573226167349621344476004471313288020398240113991699259941978112α 40       −223578855128847742913688810087057318022143978462109332025481258567127269376α 40
                                                               −168198634626680009003513480377236264968641977685259854545270514440488513175552α 39       +3806641102807223385639875513891980988734164017656312910101180605432770592768α 39
                                                               −668594708420193863217346925249650551196858552245852383052928679191604052885504α 38       +4698338022493830197418469777664958098209079184719648168122484318843776794624α 38
                                                               +829995196451920004299651167659513171123326408698056871202815263749436350660608α 37       −27472779560617412642244986656083233718762546534015558981997009063520073940992α 37
                                                              +6805400890411122172338081288981379379115027947251954438964848554500327026458624α 36       −53681346508826005770227174053581590059283954164048929404839105532796000534528α 36
                                                               −839859147076619012613401783607878586283917926703478867476334483102478263910400α 35       +159792483519832871643195761447614587325418857137220582772566606510963040452608α 35
                                                             −54336251411672379109173054554388944990018972031681985156883655345205770838867968α 34       +447775073289651418862702364745936934030540232799739862181009845955145918054400α 34
                                                             −31763834543511199735483407052389951464492348704450435677017768682913434678853632α 33       −716151822637851063198942928932119452580573299424788537816341142171636199325696α 33
                                                            +357712343186400835247921272739995225258056636329416844164038875886993432486346752α 32       −2933014614963404405624949533910517712184375976693976408790612422895031925342208α 32
                                                            +394894109850616441422196163643656479874423531345017994904270039571808903743143936α 31       +2123830137329614973540541687269913350581043300869459472923500012177964595675136α 31
                                                           −1993929054800515710688917066299914269693286626662952457319746685784090804001701888α 30       +15491595398748844916213727820453788960246908641990943232584972825253134896988160α 30
                                                           −3002267549064744794430368624087097289757148076091004127530245571997364275264880640α 29       −567958048418299255333286711763252835749000069031930133372386182151207554908160α 29
                                                           +9573037450950832796546125489519791559144293205440801001596502044790259906531819520α 28       −66470511672905973490254270449160748571544305482918584099892594998203682442444800α 28
                                                         +17344649689902103638748302705765490194768583990372876266091126135709005379492904960α 27        −41709961606955286961348486645761651227147583272088758133872408100389592005345280α 27
                                                         −40109860118705371377719161262775470420310263138301806878152530252877875499258347520α 26        +230054579604523153712298391601390663928014143964616089795553744517711724229427200α 26
                                                         −81164940713776050502710413692301000793918577563223455903690236298808582388129464320α 25        +329047428589773383037144315393721888182438735406281384979987048470391313714380800α 25
                                                        +148564684652598057008901304730992665142722743799406464890491019151228896289384038400α 24        −624457510685469088854461981456149137717339107570818384916469113052631000311398400α 24
                                                        +316011966716392521139260824696069224379619965509016982919437611079336611648687308800α 23        −1677023335298418194342571458068169568589073430891247365956379137661470030954496000α 23
                                                        −493937443242584182232311411058386151572960694882377097092991520649901348015308800000α 22        +1230550173656441248007569837874753909716280107131735470279305802166985335767040000α 22
                                                       −1032631097012698995004666052463769745257461602028357530776684844670222403998056448000α 21        +6678146820080249693682249156290720809288474225484848277349949390581823092817920000α 21
                                                       +1496051498205595023212876520305710378404801491740260076675413316113755884612485120000α 20        −1335521590342284671869409797110636836566621705504540007316206486982151246970880000α 20
                                                       +2808040259722605050478570986966436499493340536522637266197921902172159568196403200000α 19        −22059887560957847625176129319162020059098234073297049383363906396128975739944960000α 19
                                                       −4136197022520781923456607241837348242573554483478641216258357714108027813809356800000α 18        −785340364420012115414030768139171427530706940353196376588668778473179840512000000α 18
                                                       −6160485939298474432256897143548698073388765535732087612799636625216399725507379200000α 17        +61717145472396641090916430698897769773248344842243911905796114382139890755174400000α 17
                                                      +10181878863815582516096533223816217477281300683613861533575784672221983047837286400000α 16        +4618799817652795890614174914969648296550799276151584724886711273496204279808000000α 16
                                                      +10194856369622478439949806821168034096091795201083524149176484646680561362088755200000α 15        −145443881953486865648263190807202565800657985019098154597005260614892420857856000000α 15
                                                      −21187043154586589769777874169878395124445179056372063547781637907554829911195648000000α 14        +3895092842622840658053685865827455168729291218619441351061760037262624555008000000α 14
                                                      −10797617499303349106653965603456976243130791900284770633819420554804763717271552000000α 13        +280377685657177839855779204679112256388859412881172644774038185688216297799680000000α 13
                                                      +34810029936836090031365778846622873044044684351940766602929819682347633942200320000000α 12        −61886949354628165807560200683179015577169820467161436162087652305392411607040000000α 12
                                                       +2444726911101623695480273766948648572307801537702233726799335232903066482114560000000α 11        −419675757995547385956754793581818014152422427747599875638509945701343323750400000000α 11
                                                      −41556351242381300546605413427086996396993985304666948472681225427743791067955200000000α 10        +198444685626856286689595946806119633184708804987305884557282973568786130534400000000α 10
                                                       +13235190618796698664164720739564065289559223880035423917829189953498612603289600000000α 9        +448747751865602411338231508161295374031102511536034615950378207124016594944000000000α 9
                                                       +31707117886734781934293206235313206269589256456457305316683904435212337020928000000000α 8        −354225411849996408405676297836399354389793212596699228226946778751972671488000000000α 8
                                                       −22902862215982163769314078339007120966645769612414912118902053217891120054272000000000α 7        −289553838601814478908147100882111896550771868124112559407400778696805580800000000000α 7
                                                       −10861447231493527964796381160797577847506774386629058766245206345294177894400000000000α 6        +380193432519284724415876033554186663453423948344477630293719517144232755200000000000α 6
                                                       +16702340614440996726321322519580478377784196762456013465355368892183714201600000000000α 5        +49868638731749836953497035941697409493586060953068752243112234044096512000000000000α 5
                                                        −2484655700299390942962097170834933290427413703835951243538833117682860032000000000000α 4        −225214852583720088017543526212238701302651117601148886021831714815344640000000000000α 4
                                                        −4413329047578208225715144832646023361841607400402542869168917500552806400000000000000α 3        +65704820370519415064487362188463863760063365628098565999947778359296000000000000000α 3
                                                        +2487780731058996453939104246064539866264498778933228932687035282279628800000000000000α 2        +49648864534173955171275387887713942931184684832027306458656054181888000000000000000α 2
                                                          −402148158541143771038030692426712820265062425103540831235367384383488000000000000000α         −35756856984770583727093678769849105127720172150476292008503798661120000000000000000α
                                                              −5203808713264169193283107063136995887025759130647063545708229427200000000000000000        +6649311133615327302528414580675050300088470000271247863960515379200000000000000000
         Figure 2.5 – A PageRank function. x 1 (α) = (−23 6030) f (α)     (α), see section .


                                                                                          1 (s)                   = (−23/ 6030)ƒ (s)/ g(s)
                                                                                                                                                                                              Figure 2.5 (continued).
Spectral methods for linear
systems with random inputs
   A parameterized matrix view
        First     linear systems
        Second    random inputs
        Third     parameterized
                  matrices
        Fourth    spectral methods
David F. Gleich (Sandia)     Parameterized Matrices     20 / 38



   Spectral Methods

        Approximate a function in a polynomial basis!
        In UQ, known as
                  polynomial chaos
                  generalized polynomial chaos
                  stochastic Galerkin
                  stochastic collation
David F. Gleich (Sandia)           Parameterized Matrices   21 / 38



   Spectral Fourier Coefficients
        {π , ∈ N} : an orthonormal polynomial basis.
                                          ∞
                                ƒ (s) =        ƒ π π (s)
                                          =0


                           Fourier coefficients
        Truncating this representation yields best
        approximation in a mean sense.

        But how do we compute them?
David F. Gleich (Sandia)      Parameterized Matrices   22 / 38



   Computable Polynomial Approx.
        {π , ∈ N} : an orthonormal polynomial basis.
                                     ∞
                           ƒ (s) =        ƒ π π (s)
                                     =0


        Approx. 〈ƒ π 〉 with m-point Gauss quadrature


                           pseudo-spectral
David F. Gleich (Sandia)          Parameterized Matrices             23 / 38



   Gaussian Quadrature

                           b                       m
                               ƒ ( ) dω( ) =               ƒ (λ )ω
                                                   =1


        An m point quadrature rule will exactly inte-
        grate all polynomials of degree 2m − 1
        All ω > 0, all           < λ < b.
David F. Gleich (Sandia)            Parameterized Matrices   24 / 38



   Pseudospectral Methods for PMEs

                               A(s)x(s) = b(s)

                                    N−1
                           x(s) ≈         x π (s) = Xπ(s)
                                    =0
                                    m
                             x =          x(λj )π (λj )ωj
                                    j=0

                                “X = x(Λ)DQ”
David F. Gleich (Sandia)             Parameterized Matrices   25 / 38



   Galerkin Approximations for PMEs
                                A(s)x(s) = b(s)

                                     N−1
                            x(s) ≈         x π (s) = Xπ(s)
                                      =0

                           (A(s)Xπ(s) − b(s))π(s)T = 0

                           A(s)Xπ(s)π(s)T = b(s)π T

             π(s)π(s)T ⊗ A(s) vec(X) = π(s) ⊗ b(s)
                      But how do we compute them?
David F. Gleich (Sandia)          Parameterized Matrices            26 / 38



   Comparison results
                                        ρ1
                                                               ρ2
                           -1                              1

        Let ρ be the sum of semi-axes of the ellipse
        (hyperellipse) of analyticity.
        Both methods converge:

                                Cp ρ−N vs. Cg ρ−N

                            Is it even worth it?
David F. Gleich (Sandia)                      Parameterized Matrices                     27 / 38



   Convergence of approximation
                                                              SPECTRAL METHODS F


                                               0
       1+          s       0 (s)              10
        s          1       1 (s)
                                               −2
               2                              10
        =
               1
                                   L2 Error    −4
                                              10
                       2−s
       0 (s)   =
                   1 + − s2                    −6
                                              10
                   1 + − 2s                              ε=0.8
       1 (s)   =                               −8        ε=0.6
                   1 + − s2                   10
                                                         ε=0.4
                                                         ε=0.2
   Convergence rate                            −10
                                              10
   ρ<1+ .                                           0     5       10    15     20   25    30
                                                                       Order
David F. Gleich (Sandia)        Parameterized Matrices                 28 / 38



   A Gautschi-Golub comparison
         Quadrature
                   b                  m
                       ƒ ( )dω( ) ≈         ƒ (λj )ωj = eT ƒ (Jm )e1
                                                         1
                                      j=1

         where

                   Jm is the m × m Jacobi matrix for ω

         J is tridiagonal, and encodes three-term
         recurrence
David F. Gleich (Sandia)        Parameterized Matrices                    29 / 38



   A Gautschi-Golub comparison
   Pseudo-spectral                      Galerkin
     A(Jm ) vec(X) = b(Jm )e1            [A(J∞ )]m vec(X) = [b(J∞ )]m

                                        the notation [·]m means take
                                        the leading m × m block of ·.
   This solution is truncating          This solution is truncating the
   the expansion                        operator

   Computational Implication
   Given 〈(π(s)π(s)T ⊗ A(s))〉 vec(X) = 〈π(s) ⊗ b(s)〉
   Approximate 〈(π(s)π(s)T ⊗ A(s))〉, and 〈π(s) ⊗ b(s)〉 with GQ?
   NO! Equivalent to [A(Jm )]m =⇒ same answer.
   NOTE! Both equal for linear A(s), and “low-degree” polys b(s)
David F. Gleich (Sandia)    Parameterized Matrices                   30 / 38



   Computing the Galerkin solution
         ,j block of π(s)π(s)T ⊗A(s) = A(s)π (s)πj (s)
        IDEA use M > m point quadrature.
        If A(s) is a polynomial of degree d, then if

                                  m+m+d
                           “M >                      ” not precise
                                         2
        the solution will be exact.
        If A(s) is an analytic function with a rapidly
        converging expansion, large M will be close.
David F. Gleich (Sandia)             Parameterized Matrices           31 / 38



   Numerically integrated Galerkin
         ,j block of π(s)π(s)T ⊗A(s) = A(s)π (s)πj (s)
        Integrate each block with M point quadrature
        After much munging with quadrature rules

             π(s)π(s)T ⊗ A(s)               M   = (Q ⊗ )A(Λ)(Q ⊗ )T

        where                                  Q:m×M
                            A(λ1 )
                                     ...        orthogonal rows
        A(Λ) =                                 , weighted rows
               
                                         A(λM ) of J ’s eigenvecs
                           All we need is a function for A(·)
                                                      M
David F. Gleich (Sandia)     Parameterized Matrices           32 / 38



   Numerical Gakerkin factorization

             π(s)π(s)T ⊗ A(s)       M   = (Q ⊗ )A(Λ)(Q ⊗ )T


        Provides
                  computable matrix-vector product!
                  eigenvalue bounds on A( s)
                  preconditioning insights
                  a computable residual
David F. Gleich (Sandia)       Parameterized Matrices                  33 / 38



   Parameterized Matrix Package
      PMPACK
        A Matlab package for
        Parameterized Matrix Problems
        https: //github. com/paulcon/pmpack

        Implements univariate and multivariate
        Galerkin and pseudo-spectral methods
             Many demos                     Residual error estimates
             Uncertainty quanti-            Arbitrary polynomial
                fication helpers               bases (anisotropic)
             Simple interface               Many parameter types
David F. Gleich (Sandia)     Parameterized Matrices                34 / 38



   Parameterized Matrix Package
      PMPACK
        % define parameters
        s = [ uniform( 0, 1/2) , uniform( 0, 1/2) ] ;

        % for PageRank
        Av = @( x, s) x- ( s( 1) +s( 2) ) *P*x
        bs = @( s) ( 1- ( s( 1) +s( 2) ) *v           deg of polys in
                                                      Av, bs
        basis = total_order( s, 6) ;
        X = numerical_galerkin( Av, [ 1, 1] , bs, [ 1, 1] ,
                                     s, basis)
David F. Gleich (Sandia)                            Parameterized Matrices                                        35 / 38



   Where is this going?
     Beyond spectral methods!
     MapReduce and Surrogate Models
          A surrogate model
          is a function that
          reproduces the
                                          f1        Surrogate
          output of a simul-
                                                     Sample
          ation and predicts
          its output at new                    f2
          parameter values.


                                  f5

              The Database                                                                 New Samples
                                                      The Surrogate
                   s1 -> f1            Extraction                          Interpolation    sa -> fa
                   s2 -> f2                                                                 sb -> fb

                   sk -> fk                             Just one machine
                                                                                            sc -> fc
            On the MapReduce cluster                                                 On the MapReduce cluster
          David Gleich (Sandia)                            5/5/2011                                       13/18
David F. Gleich (Sandia)          Parameterized Matrices            36 / 38



   Where is this going?
     Parameterized Lanczos!                                    constant!
                           A(s)Vk (s) = Vk+1 (s)Tk,k+1

        The matrix Tk,k is the first k terms of the Ja-
        cobi matrix for the weight

                                 b(s)T A(s)b(s)

        where b(s) is the first Lanczos vector.
                       uses Chebfun for one-parameter
                       multivarite methods using Monte Carlo
David F. Gleich (Sandia)   Parameterized Matrices        37 / 38



   Summary

        Look at problems in uncertainty quantification
        as parameterized matrices
        Extended the theory of spectral methods to
        the parameterized matrix case.
        Devleoped software for spectral methods for
        parameterized matrices.
Papers
Constantine, Gleich, Iaccarino. Spectral Methods
for Parametrized Matrix Problems. SIMAX, 2010.
Constantine, Gleich, Iaccarino. A Factorization of
the Spectal Galerkin System for Parameterized
Matrix Equations: Derivation and Applications.
SISC, to appear.
Constantine, Gleich. Random Alpha PageRank.
Internet Mathematics, 2010.

Code
https: //github. com/paulcon/pmpack

More Related Content

PDF
Ben Carterett — Advances in Information Retrieval Evaluation
PDF
MATHEON Center Days: Index determination and structural analysis using Algori...
PDF
Lesson 27: Integration by Substitution (Section 041 slides)
PPT
Clustering
PDF
precRec on D2, D3
PDF
precRec on D2, D3
PDF
Lesson 1: Functions
PPT
2010-Pregel
Ben Carterett — Advances in Information Retrieval Evaluation
MATHEON Center Days: Index determination and structural analysis using Algori...
Lesson 27: Integration by Substitution (Section 041 slides)
Clustering
precRec on D2, D3
precRec on D2, D3
Lesson 1: Functions
2010-Pregel

Viewers also liked (13)

PDF
Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large ...
PDF
Spectra of Large Network
PDF
Fast pair-wise and node-wise algorithms for commute times and Katz scores
PDF
Fast Katz and Commuters
PDF
Two numerical graph algorithms
PPT
Mesopotamia
PDF
What you can do with a tall-and-skinny QR factorization in Hadoop: Principal ...
PDF
JBossWS Project by Alessio Soldano
PDF
Fast katz-presentation
DOCX
Smartboard grammar games
DOCX
PDF
Fast matrix primitives for ranking, link-prediction and more
PDF
Relaxation methods for the matrix exponential on large networks
Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large ...
Spectra of Large Network
Fast pair-wise and node-wise algorithms for commute times and Katz scores
Fast Katz and Commuters
Two numerical graph algorithms
Mesopotamia
What you can do with a tall-and-skinny QR factorization in Hadoop: Principal ...
JBossWS Project by Alessio Soldano
Fast katz-presentation
Smartboard grammar games
Fast matrix primitives for ranking, link-prediction and more
Relaxation methods for the matrix exponential on large networks
Ad

Similar to Spectral methods for linear systems with random inputs (20)

PDF
Skew-symmetric matrix completion for rank aggregation
PDF
Simulation Informatics; Analyzing Large Scientific Datasets
PDF
Reading revue of "Inferring Multiple Graphical Structures"
PDF
gans_copy.pdfhjsjsisidkskskkskwkduydjekedj
PDF
Introduction to Generative Adversarial Network
PDF
Godunov-SPH
PDF
Graph Kernels for Chemical Informatics
PDF
Participation costs dismiss the advantage of heterogeneous networks in evolut...
PDF
MUMS Opening Workshop - Quantifying Nonparametric Modeling Uncertainty with B...
PDF
Data assimilation with OpenDA
PDF
Habilitation à diriger des recherches
PDF
Introduction to Deep Generative Models
PDF
gnm: a Package for Generalized Nonlinear Models
PDF
The spectre of the spectrum
PDF
Simulation Informatics
PDF
Automatic bayesian cubature
PPT
Project3.ppt
PDF
What the matrix can tell us about the social network.
PPTX
Synthetic Image Data Generation using GAN &Triple GAN.pptx
PDF
Fast relaxation methods for the matrix exponential
Skew-symmetric matrix completion for rank aggregation
Simulation Informatics; Analyzing Large Scientific Datasets
Reading revue of "Inferring Multiple Graphical Structures"
gans_copy.pdfhjsjsisidkskskkskwkduydjekedj
Introduction to Generative Adversarial Network
Godunov-SPH
Graph Kernels for Chemical Informatics
Participation costs dismiss the advantage of heterogeneous networks in evolut...
MUMS Opening Workshop - Quantifying Nonparametric Modeling Uncertainty with B...
Data assimilation with OpenDA
Habilitation à diriger des recherches
Introduction to Deep Generative Models
gnm: a Package for Generalized Nonlinear Models
The spectre of the spectrum
Simulation Informatics
Automatic bayesian cubature
Project3.ppt
What the matrix can tell us about the social network.
Synthetic Image Data Generation using GAN &Triple GAN.pptx
Fast relaxation methods for the matrix exponential
Ad

More from David Gleich (20)

PDF
Engineering Data Science Objectives for Social Network Analysis
PDF
Correlation clustering and community detection in graphs and networks
PDF
Spectral clustering with motifs and higher-order structures
PDF
Higher-order organization of complex networks
PDF
Spacey random walks and higher-order data analysis
PDF
Non-exhaustive, Overlapping K-means
PDF
Using Local Spectral Methods to Robustify Graph-Based Learning
PDF
Spacey random walks and higher order Markov chains
PDF
Localized methods in graph mining
PDF
PageRank Centrality of dynamic graph structures
PDF
Iterative methods with special structures
PDF
Big data matrix factorizations and Overlapping community detection in graphs
PDF
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...
PDF
Localized methods for diffusions in large graphs
PDF
Anti-differentiating Approximation Algorithms: PageRank and MinCut
PDF
Gaps between the theory and practice of large-scale matrix-based network comp...
PDF
MapReduce Tall-and-skinny QR and applications
PDF
Recommendation and graph algorithms in Hadoop and SQL
PDF
Personalized PageRank based community detection
PDF
Tall and Skinny QRs in MapReduce
Engineering Data Science Objectives for Social Network Analysis
Correlation clustering and community detection in graphs and networks
Spectral clustering with motifs and higher-order structures
Higher-order organization of complex networks
Spacey random walks and higher-order data analysis
Non-exhaustive, Overlapping K-means
Using Local Spectral Methods to Robustify Graph-Based Learning
Spacey random walks and higher order Markov chains
Localized methods in graph mining
PageRank Centrality of dynamic graph structures
Iterative methods with special structures
Big data matrix factorizations and Overlapping community detection in graphs
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...
Localized methods for diffusions in large graphs
Anti-differentiating Approximation Algorithms: PageRank and MinCut
Gaps between the theory and practice of large-scale matrix-based network comp...
MapReduce Tall-and-skinny QR and applications
Recommendation and graph algorithms in Hadoop and SQL
Personalized PageRank based community detection
Tall and Skinny QRs in MapReduce

Recently uploaded (20)

PDF
Optimiser vos workloads AI/ML sur Amazon EC2 et AWS Graviton
PPTX
Big Data Technologies - Introduction.pptx
PDF
Per capita expenditure prediction using model stacking based on satellite ima...
PDF
Approach and Philosophy of On baking technology
PPTX
sap open course for s4hana steps from ECC to s4
PDF
Building Integrated photovoltaic BIPV_UPV.pdf
PDF
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Profit Center Accounting in SAP S/4HANA, S4F28 Col11
PDF
Blue Purple Modern Animated Computer Science Presentation.pdf.pdf
PPTX
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
PDF
Electronic commerce courselecture one. Pdf
PPTX
Programs and apps: productivity, graphics, security and other tools
PPTX
Cloud computing and distributed systems.
PDF
Diabetes mellitus diagnosis method based random forest with bat algorithm
PDF
Machine learning based COVID-19 study performance prediction
PPTX
Spectroscopy.pptx food analysis technology
PPTX
ACSFv1EN-58255 AWS Academy Cloud Security Foundations.pptx
PDF
Empathic Computing: Creating Shared Understanding
PDF
Advanced methodologies resolving dimensionality complications for autism neur...
Optimiser vos workloads AI/ML sur Amazon EC2 et AWS Graviton
Big Data Technologies - Introduction.pptx
Per capita expenditure prediction using model stacking based on satellite ima...
Approach and Philosophy of On baking technology
sap open course for s4hana steps from ECC to s4
Building Integrated photovoltaic BIPV_UPV.pdf
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
Digital-Transformation-Roadmap-for-Companies.pptx
Profit Center Accounting in SAP S/4HANA, S4F28 Col11
Blue Purple Modern Animated Computer Science Presentation.pdf.pdf
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
Electronic commerce courselecture one. Pdf
Programs and apps: productivity, graphics, security and other tools
Cloud computing and distributed systems.
Diabetes mellitus diagnosis method based random forest with bat algorithm
Machine learning based COVID-19 study performance prediction
Spectroscopy.pptx food analysis technology
ACSFv1EN-58255 AWS Academy Cloud Security Foundations.pptx
Empathic Computing: Creating Shared Understanding
Advanced methodologies resolving dimensionality complications for autism neur...

Spectral methods for linear systems with random inputs

  • 1. Spectral methods for linear systems with random inputs A parameterized matrix view David F. Gleich Sandia National Laboratories with Paul Constantine @ Sandia and Gianluca Iaccarino @ Stanford
  • 2. Spectral methods for linear systems with random inputs A parameterized matrix view First linear systems Second random inputs Third parameterized matrices Fourth spectral methods
  • 3. David F. Gleich (Sandia) Parameterized Matrices 3 / 38 Computational Science Discretizing Reality Start with physical model Discretize space and time Arrive at linear system or eigenvalue problem
  • 4. David F. Gleich (Sandia) Parameterized Matrices 4 / 38 Computational Science Discretizing Reality
  • 5. David F. Gleich (Sandia) Parameterized Matrices 5 / 38 Computational Science Discretizing Reality Ax = b
  • 6. David F. Gleich (Sandia) Parameterized Matrices 6 / 38 Matrices at this workshop A Random Gaussian Random sums of independent matrices Random adjacency matrices
  • 7. David F. Gleich (Sandia) Parameterized Matrices 7 / 38 Fireflies and Jellybeans, Creative Commons ∇·∇ =ƒ
  • 8. David F. Gleich (Sandia) Parameterized Matrices 8 / 38 Fireflies and Jellybeans, Creative Commons ∇ · (α(s, )∇ ) = ƒ K + s1 K1 + s2 K2 + . . . = f
  • 9. David F. Gleich (Sandia) Parameterized Matrices 9 / 38 My favorite model PAG E R A N K 3 1. follow out-edges uniformly with probability α, and 2 5 4 2. randomly jump according to v with probability 1 − α, we’ll as- 1 6 sume = 1/ n.  1/ 6 ↓ Induces a Markov chain model 1/ 2 0 0 0 0 1/ 6 0 0 1/ 3 0 0 αP + (1 − α)veT x(α) = x(α)  1/ 6 1/ 2 0 1/ 3 0 0  1/ 6 0 1/ 2 0 0 0 1/ 6 0 1/ 2 1/ 3 0 1 or the linear system 1/ 6 0 0 0 1 0 ( − αP)x(α) = (1 − α)v P
  • 10. David F. Gleich (Sandia) Parameterized Matrices 10 / 38 The PageRank Random Variable 3.0 InfBeta( 3.2 , 2.0 , 1.9e−05 , 0.0019 ) 2.5 2.0 density 1.5 1.0 0.5 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Raw α
  • 11. David F. Gleich (Sandia) Parameterized Matrices 11 / 38 Parameterized Matrices Better Discretized Reality A(s)x(s) = b(s)
  • 12. David F. Gleich (Sandia) Parameterized Matrices 12 / 38 Parameterized Matrices Better Discretized Reality A(s)x(s) = b(s) s - independent random variables/parameters bounded, analytic, non-singular
  • 13. David F. Gleich (Sandia) Parameterized Matrices 13 / 38 A Parameterized Matrix View of Uncertainty Quantification Setup A(s)x(s) = b(s) s∈D ƒ = ƒ ds D Wi l l m y c o o ki e s b u rn ? Questions E[x(s)] = 〈x(s)〉 Std[x(s)] P { (s) ≥ γ} x(s) ≈ faster y(s) Fireflies and Jellybeans, Creative Commons
  • 14. David F. Gleich (Sandia) Parameterized Matrices 14 / 38 Uncertainty Quantification At this workshop Richmond Unknown sensor array locations. Schehr Where are the viscious walkers? Antonsen Uncertain component structure. Assumed "totally" random
  • 15. David F. Gleich (Sandia) Parameterized Matrices 15 / 38 A new type of sensitivity analysis Ulam Networks on the Chirikov Map Chirikov map Ulam network yt+1 = ηyt +k sin( t +θt ) 1. divide phase space into uniform cells t+1 = t + yt+1 2. form P based on trajectories. log(E [x(A)]) log(Std [x(A)]))/ log(E [x(A)]) A ∼ Bet (2, 16) Note White is larger, black is smaller Google matrix, dynamical attractors, and Ulam networks, Shepelyansky and Zhirov, arXiv David F. Gleich (UBC) Random sensitivity Sandia 23 / 37
  • 16. David F. Gleich (Sandia) Parameterized Matrices 16 / 38 Improved web-spam classification Webspam application Hosts of uk-2006 are labeled as spam, not-spam, other P R f FP FN Baseline 0.694 0.558 0.618 0.034 0.442 Beta(0.5,1.5) 0.695 0.561 0.621 0.034 0.439 Beta(1,1) 0.698 0.562 0.622 0.033 0.438 Beta(2,16) 0.699 0.562 0.623 0.033 0.438 Note Bagged (10) J48 decision tree classifier in Weka, mean of 50 repetitions from 10-fold cross-validation of 4948 non-spam and 674 spam hosts (5622 total). Becchetti et al. Link analysis for Web spam detection, 2008. David F. Gleich (UBC) Random sensitivity Sandia 29 / 37
  • 17. David F. Gleich (Sandia) Parameterized Matrices 17 / 38 Solutions are rational or analytic A(s)x(s) = b(s) det(A (s)) (s) = det(A(s)) A = A(s) with ith column replaced by b(s)
  • 18. David F. Gleich (Sandia) Parameterized Matrices 18 / 38 A viable computational strategy? ⋅ . ⋅ f (α) = 1724683103168320512000α 102 − 351689859974563275916800α 101 + 1046657678560756011923040α 100 (α) = 21252680112847680000α 102 +332821515558986503317268308α 99 + 202994690094545539249274953458α 98 + 701216550622104187641429941160α 97 −3542775096896042918400α 101 − 377301357230918051819160α 100 + 62030166204003769204027938α 99 + 301903572553392042618587937α 98 +38942435173273232195508862504752α 96 − 5204876256969489587508598423780757α 95 − 53419116345848724180375395029139614α 94 −27515144995670593102754792187α 97 − 1391342388530090922919905979557α 96 − 11397010225845179645798293856049α 95 +1621997105501543781796265745838677670α 93 + 17992097277595516775992937444966323725α 92 +487046819801240647260974920877667α 94 + 8641748415645906110710596472701695α 93 − 14615573868254463557271968794871527α 92 −228388738389199148614341585444680228464α 91 − 2572935401339464873388154472765864295466α 90 −1455304405730842808585234463006780870α 91 − 16140532952116322684344866986683755014α 90 −18662047188535851000868073690251020472621α 89 − 155192964832717622674637679380949267008397α 88 −107685923577790689207116358432796101348α 89 + 3574857500140390342079726927167132783327α 88 +13633798075806927018912795365187923947976816α 87 + 153692481592717017931843564092779914769739855α 86 +76245995916566900197088870723441134067760α 87 − 320477613697118756563592647774688786780579α 86 −2424702525231324896856434133527720085459106818α 85 − 34112664906875644324640001664890877920583430935α 84 −14315018719450474212530996756919665488506623α 85 − 12271042346558183829899943919127664848771235α 84 +222921632950502905446093540571509314548545319158α 83 + 4458381340774458139955262362762709170337141183042α 82 +1538719934896052457300693234469902122130588440α 83 + 7259823837632938466306787148779956756499503259α 82 −9722398912749159172830586061232227612575398195577α 81 − 402863595222192101330043246404750577170418624210463α 80 −91383277962053778179963631846131934198363974003α 81 − 912158632690159715631486922494993985581191177254α 80 −241296146875962767748365749082981265577900593669099α 79 + 26884891161116233003550134767867058390000240645389885α 78 +1124589169570249225316595386438810701468062018941α 79 − 55599491760340084897708205765116975153096053881206α 78 +75002935639704657680175868562515328344632861061620026α 77 − 1355245718493528694128677343628002432897202221776993666α 76 +254197028878341726795811304127085084201803714274594α 77 − 1155102780712932745491921904562487673324953687625090α 76 −6666337432948865424681896342751813538288258918631143898α 75 + 50876562123828411130342908134923596879946044492587906688α 74 −19623309116424352882311523132748440745863270150867432α 75 − 72367264828688457023192884699324797029606326773402260α 74 +385972738637461890892793659070699381929652086327544953064α 73 − 1324370012053495348856190918458325441254102678707139546912α 72 +510591330662979105902331311824358111451756310585317896α 73 + 6560635654785580651459993551515346226540950556472012168α 72 −16416792980158036153780188009203628703318521649963318398744α 71 + 17510197624369310054645143199845105805941154913191274775360α 70 +11841946546859350197679256661965428675545845230913012752α 71 − 222422692257166102165445803087102201095333519552710152624α 70 +533320137070985354296793454864336229974212018883255863520736α 69 + 275502212308122569075672900514808641788656066608417565862128α 68 −1447290325427425453794609658098719385231428839474861685840α 69 + 2125011726240928873652963898522501443619028980101705108896α 68 −13429082722840051523544458153489421210623008268881676515202688α 67 +56163879158282775333105949842095267377034088228166264755488α 67 + 133653341840138472687713523321901358136789047544268798190144α 66 −23110058843365910555627839838104471746030299594537756688223008α 66 −1165851790876533575106055126719543401792990924852555883239232α 65 +262081257818502675810469542460738736851208401216965512926700160α 65 −7205045167922126127366881708591461911830986630512778219907200α 64 +729407390179003876249104385055674850942454472967192021090685376α 64 +8196149623293434725419276185048399130126199483584663609965696α 63 −3847937179452929633833233710422322341537775007885518269634539392α 63 +190347290617372900092754118891814664663338859287254054095265536α 62 −15488141989129507247130473020571135237573107436265881323677072000α 62 +296403177926940870392191966640325276665391672647048523475737600α 61 +36050325771659567239591241663693950811960305821938730156334667776α 61 −3179986962227253427695124755087565566711837258936975824737021952α 60 +246707867322513330007744656494007568641366676837744833157870986240α 60 −12273950891286672757637149571293897139589064857886165164957404160α 59 +66698815198854350338382524697115939758820557665663603703007667712α 59 +31408962973625270006925545397999409094566386715881351869322999808α 58 −2959446110396107328472639479854607457433633185566140760490226286592α 58 +253177395609699067378776631302481890469651122338031051366108686336α 57 −12528512804728910558071029225789548204605758683928995029146000314368α 57 −15354832074031738521204442047058295183786064138590507845987942400α 56 +19985525277247932558760938212461479524515746377831707793868714172416α 56 −3457076532174502560822426326142749948730584183953208907119801098240α 55 +343866190600408921247069416527135879796528858737524668958998645633024α 55 −6661437625275114934838338879511817915494254490727882100057772130304α 54 +237159992339459130849980507259488489676582642639199883151854812422144α 54 +28704083600179676384022705580143799967745682382583318411010759639040α 53 −6150352682504179603648657901968989091083378789857325448622418220859392α 53 +173119877625293135511416194747967318688771201702803231109775079243776α 52 −12507084588874068660420542622454441021005365876210831205762085535989760α 52 +42285615967170654345485778244291908234053330314299949447131636826112α 51 +76052343558405304817491728967709919562879906814237879556140479278219264α 51 −3092545165791022831669116892040565590342926023532342815170675350831104α 50 +281657470545819893901842735393494111347269819443029672934492155921629184α 50 −7385454932946443098573906964601689327710122151758555775183630113177600α 49 −524010169549932716315240835391286383538294517356494888193446880264060928α 49 +44090325705050939960465955316629060665099648652920301218388343039721472α 48 −4283228548253488673520351046009849054273946705738400536855052450584985600α 48 +180430494757250498411208705426475191214202221095549279916495110854934528α 47 −2155194129185085332436034710334032595487897368550943059587873095183237120α 47 ƒ (s) −493525709032718650057526281767644848135900953167613963100373354560880640α 46 +44942983365390912258646063248936155917171235534162037124027584790839951360α 46 +123764976043225311633569878034493895722302903722502785220748272524591104000α 45 −3036843091999605016958964058463815080108229170215714733277797291506270208α 45 g(s) −263604612819883334094471942440378055857630908721587551326277602165812887552α 44 +3865732160987803528525842299699004166440912343665407865787648656852123648α 44 −2043045823645899057845901056050369454115577248500633141166053687383937777664α 43 +40165478772124194334610082404062794103423683134161618111009172215000203264α 43 −883572534249006235663814128436259426227447113734226469390794110452279279616α 42 −11270446090439842262616868429380066718469755470664378191173671836048162816α 42 +22029266389692672474905374638580604237511322238870051881693348503640495620096α 41 −431725269187383778295706776607285692623377582173153891079752971949306806272α 41 +45203159614332573226167349621344476004471313288020398240113991699259941978112α 40 −223578855128847742913688810087057318022143978462109332025481258567127269376α 40 −168198634626680009003513480377236264968641977685259854545270514440488513175552α 39 +3806641102807223385639875513891980988734164017656312910101180605432770592768α 39 −668594708420193863217346925249650551196858552245852383052928679191604052885504α 38 +4698338022493830197418469777664958098209079184719648168122484318843776794624α 38 +829995196451920004299651167659513171123326408698056871202815263749436350660608α 37 −27472779560617412642244986656083233718762546534015558981997009063520073940992α 37 +6805400890411122172338081288981379379115027947251954438964848554500327026458624α 36 −53681346508826005770227174053581590059283954164048929404839105532796000534528α 36 −839859147076619012613401783607878586283917926703478867476334483102478263910400α 35 +159792483519832871643195761447614587325418857137220582772566606510963040452608α 35 −54336251411672379109173054554388944990018972031681985156883655345205770838867968α 34 +447775073289651418862702364745936934030540232799739862181009845955145918054400α 34 −31763834543511199735483407052389951464492348704450435677017768682913434678853632α 33 −716151822637851063198942928932119452580573299424788537816341142171636199325696α 33 +357712343186400835247921272739995225258056636329416844164038875886993432486346752α 32 −2933014614963404405624949533910517712184375976693976408790612422895031925342208α 32 +394894109850616441422196163643656479874423531345017994904270039571808903743143936α 31 +2123830137329614973540541687269913350581043300869459472923500012177964595675136α 31 −1993929054800515710688917066299914269693286626662952457319746685784090804001701888α 30 +15491595398748844916213727820453788960246908641990943232584972825253134896988160α 30 −3002267549064744794430368624087097289757148076091004127530245571997364275264880640α 29 −567958048418299255333286711763252835749000069031930133372386182151207554908160α 29 +9573037450950832796546125489519791559144293205440801001596502044790259906531819520α 28 −66470511672905973490254270449160748571544305482918584099892594998203682442444800α 28 +17344649689902103638748302705765490194768583990372876266091126135709005379492904960α 27 −41709961606955286961348486645761651227147583272088758133872408100389592005345280α 27 −40109860118705371377719161262775470420310263138301806878152530252877875499258347520α 26 +230054579604523153712298391601390663928014143964616089795553744517711724229427200α 26 −81164940713776050502710413692301000793918577563223455903690236298808582388129464320α 25 +329047428589773383037144315393721888182438735406281384979987048470391313714380800α 25 +148564684652598057008901304730992665142722743799406464890491019151228896289384038400α 24 −624457510685469088854461981456149137717339107570818384916469113052631000311398400α 24 +316011966716392521139260824696069224379619965509016982919437611079336611648687308800α 23 −1677023335298418194342571458068169568589073430891247365956379137661470030954496000α 23 −493937443242584182232311411058386151572960694882377097092991520649901348015308800000α 22 +1230550173656441248007569837874753909716280107131735470279305802166985335767040000α 22 −1032631097012698995004666052463769745257461602028357530776684844670222403998056448000α 21 +6678146820080249693682249156290720809288474225484848277349949390581823092817920000α 21 +1496051498205595023212876520305710378404801491740260076675413316113755884612485120000α 20 −1335521590342284671869409797110636836566621705504540007316206486982151246970880000α 20 +2808040259722605050478570986966436499493340536522637266197921902172159568196403200000α 19 −22059887560957847625176129319162020059098234073297049383363906396128975739944960000α 19 −4136197022520781923456607241837348242573554483478641216258357714108027813809356800000α 18 −785340364420012115414030768139171427530706940353196376588668778473179840512000000α 18 −6160485939298474432256897143548698073388765535732087612799636625216399725507379200000α 17 +61717145472396641090916430698897769773248344842243911905796114382139890755174400000α 17 +10181878863815582516096533223816217477281300683613861533575784672221983047837286400000α 16 +4618799817652795890614174914969648296550799276151584724886711273496204279808000000α 16 +10194856369622478439949806821168034096091795201083524149176484646680561362088755200000α 15 −145443881953486865648263190807202565800657985019098154597005260614892420857856000000α 15 −21187043154586589769777874169878395124445179056372063547781637907554829911195648000000α 14 +3895092842622840658053685865827455168729291218619441351061760037262624555008000000α 14 −10797617499303349106653965603456976243130791900284770633819420554804763717271552000000α 13 +280377685657177839855779204679112256388859412881172644774038185688216297799680000000α 13 +34810029936836090031365778846622873044044684351940766602929819682347633942200320000000α 12 −61886949354628165807560200683179015577169820467161436162087652305392411607040000000α 12 +2444726911101623695480273766948648572307801537702233726799335232903066482114560000000α 11 −419675757995547385956754793581818014152422427747599875638509945701343323750400000000α 11 −41556351242381300546605413427086996396993985304666948472681225427743791067955200000000α 10 +198444685626856286689595946806119633184708804987305884557282973568786130534400000000α 10 +13235190618796698664164720739564065289559223880035423917829189953498612603289600000000α 9 +448747751865602411338231508161295374031102511536034615950378207124016594944000000000α 9 +31707117886734781934293206235313206269589256456457305316683904435212337020928000000000α 8 −354225411849996408405676297836399354389793212596699228226946778751972671488000000000α 8 −22902862215982163769314078339007120966645769612414912118902053217891120054272000000000α 7 −289553838601814478908147100882111896550771868124112559407400778696805580800000000000α 7 −10861447231493527964796381160797577847506774386629058766245206345294177894400000000000α 6 +380193432519284724415876033554186663453423948344477630293719517144232755200000000000α 6 +16702340614440996726321322519580478377784196762456013465355368892183714201600000000000α 5 +49868638731749836953497035941697409493586060953068752243112234044096512000000000000α 5 −2484655700299390942962097170834933290427413703835951243538833117682860032000000000000α 4 −225214852583720088017543526212238701302651117601148886021831714815344640000000000000α 4 −4413329047578208225715144832646023361841607400402542869168917500552806400000000000000α 3 +65704820370519415064487362188463863760063365628098565999947778359296000000000000000α 3 +2487780731058996453939104246064539866264498778933228932687035282279628800000000000000α 2 +49648864534173955171275387887713942931184684832027306458656054181888000000000000000α 2 −402148158541143771038030692426712820265062425103540831235367384383488000000000000000α −35756856984770583727093678769849105127720172150476292008503798661120000000000000000α −5203808713264169193283107063136995887025759130647063545708229427200000000000000000 +6649311133615327302528414580675050300088470000271247863960515379200000000000000000 Figure 2.5 – A PageRank function. x 1 (α) = (−23 6030) f (α) (α), see section . 1 (s) = (−23/ 6030)ƒ (s)/ g(s) Figure 2.5 (continued).
  • 19. Spectral methods for linear systems with random inputs A parameterized matrix view First linear systems Second random inputs Third parameterized matrices Fourth spectral methods
  • 20. David F. Gleich (Sandia) Parameterized Matrices 20 / 38 Spectral Methods Approximate a function in a polynomial basis! In UQ, known as polynomial chaos generalized polynomial chaos stochastic Galerkin stochastic collation
  • 21. David F. Gleich (Sandia) Parameterized Matrices 21 / 38 Spectral Fourier Coefficients {π , ∈ N} : an orthonormal polynomial basis. ∞ ƒ (s) = ƒ π π (s) =0 Fourier coefficients Truncating this representation yields best approximation in a mean sense. But how do we compute them?
  • 22. David F. Gleich (Sandia) Parameterized Matrices 22 / 38 Computable Polynomial Approx. {π , ∈ N} : an orthonormal polynomial basis. ∞ ƒ (s) = ƒ π π (s) =0 Approx. 〈ƒ π 〉 with m-point Gauss quadrature pseudo-spectral
  • 23. David F. Gleich (Sandia) Parameterized Matrices 23 / 38 Gaussian Quadrature b m ƒ ( ) dω( ) = ƒ (λ )ω =1 An m point quadrature rule will exactly inte- grate all polynomials of degree 2m − 1 All ω > 0, all < λ < b.
  • 24. David F. Gleich (Sandia) Parameterized Matrices 24 / 38 Pseudospectral Methods for PMEs A(s)x(s) = b(s) N−1 x(s) ≈ x π (s) = Xπ(s) =0 m x = x(λj )π (λj )ωj j=0 “X = x(Λ)DQ”
  • 25. David F. Gleich (Sandia) Parameterized Matrices 25 / 38 Galerkin Approximations for PMEs A(s)x(s) = b(s) N−1 x(s) ≈ x π (s) = Xπ(s) =0 (A(s)Xπ(s) − b(s))π(s)T = 0 A(s)Xπ(s)π(s)T = b(s)π T π(s)π(s)T ⊗ A(s) vec(X) = π(s) ⊗ b(s) But how do we compute them?
  • 26. David F. Gleich (Sandia) Parameterized Matrices 26 / 38 Comparison results ρ1 ρ2 -1 1 Let ρ be the sum of semi-axes of the ellipse (hyperellipse) of analyticity. Both methods converge: Cp ρ−N vs. Cg ρ−N Is it even worth it?
  • 27. David F. Gleich (Sandia) Parameterized Matrices 27 / 38 Convergence of approximation SPECTRAL METHODS F 0 1+ s 0 (s) 10 s 1 1 (s) −2 2 10 = 1 L2 Error −4 10 2−s 0 (s) = 1 + − s2 −6 10 1 + − 2s ε=0.8 1 (s) = −8 ε=0.6 1 + − s2 10 ε=0.4 ε=0.2 Convergence rate −10 10 ρ<1+ . 0 5 10 15 20 25 30 Order
  • 28. David F. Gleich (Sandia) Parameterized Matrices 28 / 38 A Gautschi-Golub comparison Quadrature b m ƒ ( )dω( ) ≈ ƒ (λj )ωj = eT ƒ (Jm )e1 1 j=1 where Jm is the m × m Jacobi matrix for ω J is tridiagonal, and encodes three-term recurrence
  • 29. David F. Gleich (Sandia) Parameterized Matrices 29 / 38 A Gautschi-Golub comparison Pseudo-spectral Galerkin A(Jm ) vec(X) = b(Jm )e1 [A(J∞ )]m vec(X) = [b(J∞ )]m the notation [·]m means take the leading m × m block of ·. This solution is truncating This solution is truncating the the expansion operator Computational Implication Given 〈(π(s)π(s)T ⊗ A(s))〉 vec(X) = 〈π(s) ⊗ b(s)〉 Approximate 〈(π(s)π(s)T ⊗ A(s))〉, and 〈π(s) ⊗ b(s)〉 with GQ? NO! Equivalent to [A(Jm )]m =⇒ same answer. NOTE! Both equal for linear A(s), and “low-degree” polys b(s)
  • 30. David F. Gleich (Sandia) Parameterized Matrices 30 / 38 Computing the Galerkin solution ,j block of π(s)π(s)T ⊗A(s) = A(s)π (s)πj (s) IDEA use M > m point quadrature. If A(s) is a polynomial of degree d, then if m+m+d “M > ” not precise 2 the solution will be exact. If A(s) is an analytic function with a rapidly converging expansion, large M will be close.
  • 31. David F. Gleich (Sandia) Parameterized Matrices 31 / 38 Numerically integrated Galerkin ,j block of π(s)π(s)T ⊗A(s) = A(s)π (s)πj (s) Integrate each block with M point quadrature After much munging with quadrature rules π(s)π(s)T ⊗ A(s) M = (Q ⊗ )A(Λ)(Q ⊗ )T where   Q:m×M A(λ1 ) ...  orthogonal rows A(Λ) =   , weighted rows  A(λM ) of J ’s eigenvecs All we need is a function for A(·) M
  • 32. David F. Gleich (Sandia) Parameterized Matrices 32 / 38 Numerical Gakerkin factorization π(s)π(s)T ⊗ A(s) M = (Q ⊗ )A(Λ)(Q ⊗ )T Provides computable matrix-vector product! eigenvalue bounds on A( s) preconditioning insights a computable residual
  • 33. David F. Gleich (Sandia) Parameterized Matrices 33 / 38 Parameterized Matrix Package PMPACK A Matlab package for Parameterized Matrix Problems https: //github. com/paulcon/pmpack Implements univariate and multivariate Galerkin and pseudo-spectral methods Many demos Residual error estimates Uncertainty quanti- Arbitrary polynomial fication helpers bases (anisotropic) Simple interface Many parameter types
  • 34. David F. Gleich (Sandia) Parameterized Matrices 34 / 38 Parameterized Matrix Package PMPACK % define parameters s = [ uniform( 0, 1/2) , uniform( 0, 1/2) ] ; % for PageRank Av = @( x, s) x- ( s( 1) +s( 2) ) *P*x bs = @( s) ( 1- ( s( 1) +s( 2) ) *v deg of polys in Av, bs basis = total_order( s, 6) ; X = numerical_galerkin( Av, [ 1, 1] , bs, [ 1, 1] , s, basis)
  • 35. David F. Gleich (Sandia) Parameterized Matrices 35 / 38 Where is this going? Beyond spectral methods! MapReduce and Surrogate Models A surrogate model is a function that reproduces the f1 Surrogate output of a simul- Sample ation and predicts its output at new f2 parameter values. f5 The Database New Samples The Surrogate s1 -> f1 Extraction Interpolation sa -> fa s2 -> f2 sb -> fb sk -> fk Just one machine sc -> fc On the MapReduce cluster On the MapReduce cluster David Gleich (Sandia) 5/5/2011 13/18
  • 36. David F. Gleich (Sandia) Parameterized Matrices 36 / 38 Where is this going? Parameterized Lanczos! constant! A(s)Vk (s) = Vk+1 (s)Tk,k+1 The matrix Tk,k is the first k terms of the Ja- cobi matrix for the weight b(s)T A(s)b(s) where b(s) is the first Lanczos vector. uses Chebfun for one-parameter multivarite methods using Monte Carlo
  • 37. David F. Gleich (Sandia) Parameterized Matrices 37 / 38 Summary Look at problems in uncertainty quantification as parameterized matrices Extended the theory of spectral methods to the parameterized matrix case. Devleoped software for spectral methods for parameterized matrices.
  • 38. Papers Constantine, Gleich, Iaccarino. Spectral Methods for Parametrized Matrix Problems. SIMAX, 2010. Constantine, Gleich, Iaccarino. A Factorization of the Spectal Galerkin System for Parameterized Matrix Equations: Derivation and Applications. SISC, to appear. Constantine, Gleich. Random Alpha PageRank. Internet Mathematics, 2010. Code https: //github. com/paulcon/pmpack