A MODIFIED REGULARIZED NEWTON METHOD FOR UNCONSTRAINED NONCONVEX OPTIMIZATION
|
|
- Bruno Cook
- 5 years ago
- Views:
Transcription
1 JRRAS 9 Ma 4 wwwarpapresscom/volumes/vol9ssue/jrras_9 6p A MODFED REGULARZED NEWON MEOD FOR UNCONSRANED NONCONVEX OPMZAON e Wa Me Q & abo Wa Collee o Scece Uverst o Shaha or Scece a echolo Shaha Cha 93 Emal: waheusst@6com ABSRAC ths paper we preset a moe reularze Newto metho or the ucostrae ocove optmzato b us trust reo techque We show that the raet a essa o the objectve ucto are Lpschtz cotuous the the moe reularze Newto metho M-RNM has a lobal coverece propert Numercal results show that the alorthm s ver ecet Kewors: Reulare Newto metho; rust reo metho; Ucostrae ocove optmzato; Global coverece MSC: M5 NRODUCON We coser the ucostrae optmzato problem m R where R : R s twce cotuousl eretable whose raet a essa are eote b a respectvel rouhout ths paper we assume that the soluto set o s oempt a eote b S a all cases reers to the -orm t s well ow that s cove a ol s smmetrc postve semete or all R Moreover s cove the S a ol s a soluto o the sstem o olear equatos ece we coul et the mmzer o b solv []-[3]he Newto metho s oe o a ecet soluto metho At ever terato t computes the tral step N 3 where a As we ow s Lpschtz cotuous a osular at the soluto the the Newto metho has quaratc coverece owever ths metho has a obvous savatae whe the s sular or ear sular o overcome the cult cause b the possble sulart o metho where the tral step s the soluto o the lear equatos where s the ett matr parameter [4] propose a reularze Newto 4 s a postve parameter whch s upate rom trato to terato he s trouce to overcome the cultes cause b the sulart or ear sulart o the essa Now we ee to coser aother questo "how to choose the moe reularze parameter? " whch wll pla mportat roles ot ol theoretcal aalss but also umercal epermets Yamashta a Fuushma [5] chose a showe that the reularze Newto metho has quaratc coverece uer the local error bou 77
2 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho coto whch s weaer tha osulart Fa a Yua [6] too wth [] a prove that the Leveber-Marqulart metho preserves the quaratc coverece uer the same cotos ths paper we wll choose the parameter as where s a postve costat ma > s upate rom terato to terato b a trust reo techque m most past stues [7]-[9] or the reularze Newto metho the coverece propertes have bee scusse ol whe s cove ths paper we propose a moe Newto metho or whose objectve ucto s ocove whch s mal motvate [] Do-ul Masao Fuushma Lqu Q a Nobuo Yamashta have propose that reularze Newto a eact Newto methos are possble etee to ocove mmzato problems [] he chose to sats where C > s a costat a ma C 5 s the mmum eevalue o ma m Base o the better perormace o the moe reularze metho wth we wll coser the choce o ma 6 m ths paper We ete the reularze Newto metho 4 to the ucostrae ocove optmzato At the -th terato o the M-RNM we set reularze parameter as > From the eto o the matr where a s postve semete eve s ocove hereore the we ca use reularze Newto metho to solve the problem o he ma scheme o the moe reularze Newto metho or ucostrae ocove optmzato s ve as ollows At ever terato t solves the lear equatos to obta the Newto step where to obta the appromate Newto step 7 a the solves the lear equatos wth 8 he purpose o ths paper s to vestate whether the propose metho or ucostrae ocove optmzato has lobal coverece he paper s oraze as ollows secto we preset a ew moe reularze Newto alorthm b us trust reo techque the prove the lobal coverece secto 3 Numercal results are ve Fall we coclue the paper the secto 4 E ALGORM AND GLOBAL CONVERGENCE ths secto we rst preset the ew moe reularze Newto alorthm b us trust reo techque the prove the lobal coverece Frst we ve the moe reularze Newto alorthm Let ete a techque be ve b 7 a 8 respectvel Sce the matr s a escet recto o at but s smmetrc a postve ma ot be ece we preer to use a trust reo 78
3 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho 79 Dee the actual reucto o at the -th terato as Are Note that the Newto step s the mmzer o the problem: m R we let the t ca be prove [] that s also a soluto o the trust reo problem: m R s t B the amous result ve b Powell [] we ow that m Smlar to s ot ol the mmzer o the problem: m R but also the soluto o the ollow trust reo problem: m R s t where hereore we also have m 3 Base o the equaltes a 3 t s reasoable or us to ee the ew precte reucto as e 4 whch satses m m e 5 he rato o the actual reucto to the precte reucto e Are r 6 plas a e role ec whether to accept the tral step a how to ajust the reularze parameter
4 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho he reularze Newto alorthm wth correcto or ucostrae ocove optmzato problems s state as ollows 3; Alorthm Step Gve R > m > < c c c < < p << p : Step the stop Otherwse o to step 3 Step 3 Compute ma m Solve 7 Step 4 Compute to obta Set Solve to obta Set r Set Step 5 Upate as Are e 8 s s r c 9 otherwse Set : a o step 3; p r < c r c c map m r > c Alorthm the ve postve costat m s the lowerbou o t plas the role prevet the step rom be too lare whe the sequece s ear the mmzer set Beore scuss the lobal coverece o the alorthm above we mae the ollow assumpto Assumpto a are both Lpschtz cotuous that s there ests a costat > such that L L > 8
5 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho a t ollows rom that L R L R L R 3 he ollow lemma ve below shows the relatoshp betwee the postve semete matr a smmetrc postve semete matr Lemma3 A real-value matr s postve semete a ol A / oo See [] Net we ve the bous o a postve ete matr a ts verse Lemma4 Suppose A s smmetrc postve semete he a hol or a > oo A A t ollows rom Lemma 3 a the eto o the -orm that A ma ma ma A A A A A A A s postve semete where A A meas the larest eevalue o A A Smlarl we have ma hs completes the proo So we have the ollow results A ma ma m A A A A A A A A heorem 5 Uer the cotos o Assumpto s boue below the Alorthm termates te teratos or satses oo that lm We prove b cotracto the theorem s ot true the there ests a postve a a teer 4 such 5 8
6 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho 8 Wthout loss o eeralt we ca suppose Set he Now we wll aalss two cases whether s te or ot Case : s te he there ests a teer such that B 9 we have < c r hereore b a 5 we euce 6 Sce we et rom 7 a 6 that 7 From 8 we obta L 8 where s a postve costat t ollows rom a 4 that o o e Are 9 Moreover rom 55 a 7 we have m L e or sucetl lare Duo to 9 a we et
7 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho 83 m o o L e e Are r whch mples that r ece there ests postve costat such that hols or all lare whch cotracts to 6 Case : s te he we have rom 5 a 5 that m m m lm > L c c e c whch mples that lm 3 he above equalt toether wth the upat rule o meas 4 Smlar to 8 t ollows rom 3 a 4 that 3 or some postve costat 3 he we have 3 s hs equalt toether wth els < s whch mples that 5
8 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho t ollows rom a 8 that Sce whch meas 6 rom 7 we have rom 5 a 6 that B the same aalss as we ow that ece there ests a postve costat cotracto to 7 he proo s complete L > m such that r 8 hols or all sucetl lare whch ves a 3 NUMERCAL EXPERMENS ths secto we test the perormace o the moe reularze Newto metho a the reularze Newto metho wthout correcto he ucto to be mmze s where are costats t s clear that ucto s mmzer set o S R s ocove a the t ca also be prove that proves a local error bou ear the mmzer o s ve b he essa 3 where 5 a a b 3 5 b 4 c c 84
9 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho Matr s ete or all but sular as the sum o ever colum s zero Sce the essa s alwas sular the Newto metho caot be use to solve olear equatos But b us the reularzato techque both reularze Newto metho a Alorthm wor qute well We teste the moe reularze Newto metho or problem where s ve b 3 wth eret values o a We set c c 5 c 75 p 5 p 4 5 m or Alorthm a able reports the orms o Star rom at ever terato whe a the Alorthm ol taes ve teratos to obta the mmzer o at each terato able lsts the orm o e- 633e- 94e-4 663e- able : Results o M-RNM o 3 he results able relect the coverece propert o he eerate teratos are We ma observe that the whole sequece coveres to We also ra the reularze Newto alorthm RNA [] wthout correcto that s we o ot solve the lear equatos 8 a just set the soluto o 7 to be the tral step s he we teste the reularze Newto alorthm [] wthout correcto a moe reularze Newto alorthm or varous o a eret choces o the start pot he results are lste able a able 3 : the selecte value o : the selecte value o ; Dm: the meso o the problem; : the th elemet ; m: the umber o teratos requre; : the al value o ; the stopp crtero : the al value o We use ; 5 as 85
10 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho Dm m 3/ 63e-7 55 / / 85e / 48e-6 55 / 9/ 48e-6 9 6/ 6e-6 55 / 5/ 5e /6 63e-7 55 / 8/8 8e / 66e-6 55 / 8/ 56e /3 384e- 55 / 5/4 486e-9 9 8/ 495e-6 55 / /5 3e /9 345e-7 55 / /5 548e /4 956e-8 55 / 5/ 9e /6 77e-7 55 / 9/ 3e /35 485e-7 55 / 9/ 8e / 4e-7 55 / 9/ 56e-6 36 able : Results o RNA a Alorthm 86
11 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho Dm m 4/6 43e-6 55 / 5/ 35e / e-7 55 / 6/ 6e-7 9 /55 74e-7 55 / 5/ 45e /8 685e-7 55 / 6/ 589e / 45e-8 55 / 3/ 543e /3 33e-8 55 / /5 756e /4 48e-7 55 / 5/7 7e /6 98e-7 55 / 5/7 454e /4 537e-8 55 / /4 379e /6 4e-9 55 / 7/ 33e-7 9 6/35 4e-7 55 / 8/9 8e /3 75e-7 55 / 38/ 47e-7 36 able 3: Results o RNA a Alorthm Moreover we ca see that or the same a able a able 3 show that the correcto term oes help to mprove reularze Newto alorthm whe the tal pot s ar awa rom the mmzer hese acts cate that the troucto o correcto s reall useul a coul accelerate the coverece o the reularze Newto metho 4 CONCLUSON We propose a moe reularze Newto metho or ucostrae ocove optmzato a show that the raet a essa o the objectve ucto are Lpschtz cotuous the the M-RNM has a lobal coverece he presete umercal results show that the practcal o the propose metho We ma raw the cocluso that our metho s better tha the reularze Newto alorthm RNM[] wthout correcto 5 REFERENCES [] W Su Y Yua Optmzato heor a Methos Sprer Scece a Busess Mea LLC New Yor 6 [] W Zhou D L A loball coveret BFGS metho or olear mootoe equatos wthout a mert uctos Math Comp [3] CKelle teratve Methos or Optmzato : Froters Apple Mathematcs vol 8 SAM Phlaelpha 999 [4] D Su A reularzato Newto metho or solv olear complemetart problems Appl Math 87
12 JRRAS 9 Ma 4 Wa et al A Moe Reularze Newto Metho Optm4 999 pp [5] D L M Fuushma L Q a N Yamashta Reularze Newto methos or cove mmzato problems wth sular solutos Comput OptmAppl 8 4 pp 3-47 [6] JYFa a YXYua O the quaratc coverece o the Leveber-Marquart metho wthout osulart assumpto Comput 74 5 pp 3-39 [7] JaFa YaaYua A reularze Newto metho or mootoe olear equatos a ts applcato Optmzato Methos a Sotware 9 4 pp -9 [8] Yamashta N Fuushma O the rate o coverece o the Leveber-Maruart metho Comput SupplWe 5 pp 7-38 [9] Pola RA Reularze Newto metho or ucostrae cove optmzato Math oram Ser B 9 pp 5-45 [] Do-huL Masao Fuushma Lqu Q Nobuo Yamashta Reularze Newto methos or cove mmzato problems wth sular solutos Computatoal Optmzato a Applcatos 8 4 pp 3-47 [] MJD Powell Coverece propertes o a class o mmzato alorthms : OL Maasara RR Meer SM Robso Es : Nolear oramm vol Acaemc ess New Yor 975 pp -7 88
A nonsmooth Levenberg-Marquardt method for generalized complementarity problem
ISSN 746-7659 Egla UK Joural of Iformato a Computg Scece Vol. 7 No. 4 0 pp. 67-7 A osmooth Leveberg-Marquart metho for geeralze complemetarty problem Shou-qag Du College of Mathematcs Qgao Uversty Qgao
More informationA NEW PREVIOUS SEARCH DIRECTION MULTIPLIER FOR NONLINEAR CONJUGATE GRADIENT METHOD
Jural Karya Asl Lorea Ahl Matemat Vol 7 No 05 Pae 006-05 Jural Karya Asl Lorea Ahl Matemat A NEW PREVIOUS SEARCH DIRECION MULIPLIER FOR NONLINEAR CONJUGAE GRADIEN MEHOD Ahma Alhawarat a, Mustafa Mamat
More informationLINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD
Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer
More informationUNIT 7 RANK CORRELATION
UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7
More informationAbstract. 1. Introduction
Joura of Mathematca Sceces: Advaces ad Appcatos Voume 4 umber 2 2 Pages 33-34 COVERGECE OF HE PROJECO YPE SHKAWA ERAO PROCESS WH ERRORS FOR A FE FAMY OF OSEF -ASYMPOCAY QUAS-OEXPASVE MAPPGS HUA QU ad S-SHEG
More informationLinear regression (cont) Logistic regression
CS 7 Fouatos of Mache Lear Lecture 4 Lear reresso cot Lostc reresso Mlos Hausrecht mlos@cs.ptt.eu 539 Seott Square Lear reresso Vector efto of the moel Iclue bas costat the put vector f - parameters ehts
More informationA new type of optimization method based on conjugate directions
A ew type of optmzato method based o cojugate drectos Pa X Scece School aj Uversty of echology ad Educato (UE aj Cha e-mal: pax94@sacom Abstract A ew type of optmzato method based o cojugate drectos s
More informationLecture Notes 2. The ability to manipulate matrices is critical in economics.
Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets
More informationJacobian-Free Diagonal Newton s Method for Solving Nonlinear Systems with Singular Jacobian ABSTRACT INTRODUCTION
Malaysa Joural of Mathematcal Sceces 5(: 4-55 ( Jacoba-ree Dagoal Newto s Method for Solvg Nolear Systems wth Sgular Jacoba Mohammed Wazr Yusuf,, Leog Wah Jue ad, Mal Abu Hassa aculty of Scece, Uverst
More informationA Fixed Point Method for Convex Systems
Appled Mathematcs 3 37-333 http://ddoor/436/am3389 Publshed Ole October (http://wwwscrpor/oural/am) A Fed Pot Method for Cove Systems Morteza Kmae Farzad Rahpeyma Departmet of Mathematcs Raz Uversty Kermashah
More information= 2. Statistic - function that doesn't depend on any of the known parameters; examples:
of Samplg Theory amples - uemploymet househol cosumpto survey Raom sample - set of rv's... ; 's have ot strbuto [ ] f f s vector of parameters e.g. Statstc - fucto that oes't epe o ay of the ow parameters;
More informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationAssignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix
Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationGeneralized Linear Regression with Regularization
Geeralze Lear Regresso wth Regularzato Zoya Bylsk March 3, 05 BASIC REGRESSION PROBLEM Note: I the followg otes I wll make explct what s a vector a what s a scalar usg vec t or otato, to avo cofuso betwee
More information9.1 Introduction to the probit and logit models
EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos
More informationEffective Modified Hybrid Conjugate Gradient Method for Large-Scale Symmetric Nonlinear Equations
Avalable at http://pvamuedu/aam Appl Appl Math ISSN: 9-9466 Vol Iue December 07 pp 06-056 Applcato ad Appled Mathematc: A Iteratoal Joural AAM Eectve Moded Hbrd Cojuate Gradet Method or Lare-Scale Smmetrc
More information1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3
Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto
More informationC.11 Bang-bang Control
Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of
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 informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationStrong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity
BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout
More informationPower Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1
ower Flow uses wth ether or both Geerator Load G G G D D 4 5 D4 D5 ecto G Net Comple ower ecto - D D ecto s egatve sg at load bus = _ G D mlarl Curret ecto = G _ D At each bus coservato of comple power
More information. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)
Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org
More informationRegression and the LMS Algorithm
CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a
More informationUNIT 6 CORRELATION COEFFICIENT
UNIT CORRELATION COEFFICIENT Correlato Coeffcet Structure. Itroucto Objectves. Cocept a Defto of Correlato.3 Tpes of Correlato.4 Scatter Dagram.5 Coeffcet of Correlato Assumptos for Correlato Coeffcet.
More informationENGI 4430 Numerical Integration Page 5-01
ENGI 443 Numercal Itegrato Page 5-5. Numercal Itegrato I some o our prevous work, (most otaly the evaluato o arc legth), t has ee dcult or mpossle to d the dete tegral. Varous symolc algera ad calculus
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationECON 5360 Class Notes GMM
ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets
More information18.413: Error Correcting Codes Lab March 2, Lecture 8
18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse
More informationJohns Hopkins University Department of Biostatistics Math Review for Introductory Courses
Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s
More information( x) min. Nonlinear optimization problem without constraints NPP: then. Global minimum of the function f(x)
Objectve fucto f() : he optzato proble cossts of fg a vector of ecso varables belogg to the feasble set of solutos R such that It s eote as: Nolear optzato proble wthout costrats NPP: R f ( ) : R R f f
More informationUnimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods
Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal
More informationJohns Hopkins University Department of Biostatistics Math Review for Introductory Courses
Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s
More informationPENALTY FUNCTIONS FOR THE MULTIOBJECTIVE OPTIMIZATION PROBLEM
Joual o Mathematcal Sceces: Advaces ad Applcatos Volume 6 Numbe 00 Pages 77-9 PENALTY FUNCTIONS FOR THE MULTIOBJECTIVE OPTIMIZATION PROBLEM DAU XUAN LUONG ad TRAN VAN AN Depatmet o Natual Sceces Quag Nh
More informationESS Line Fitting
ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here
More information4. Standard Regression Model and Spatial Dependence Tests
4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.
More informationHamilton s principle for non-holonomic systems
Das Hamltosche Przp be chtholoome Systeme, Math. A. (935), pp. 94-97. Hamlto s prcple for o-holoomc systems by Georg Hamel Berl Traslate by: D. H. Delphech I the paper Le prcpe e Hamlto et l holoomsme,
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationFEEDBACK STABILIZATION METHODS FOR THE SOLUTION OF NONLINEAR PROGRAMMING PROBLEMS
FEEDACK STAILIZATION METODS FO TE SOLUTION OF NONLINEA POGAMMING POLEMS Iasso Karafylls Departmet of Evrometal Eeer, Techcal Uversty of Crete,700, Chaa, Greece emal: arafyl@eve.tuc.r Abstract I ths wor
More informationArithmetic Mean and Geometric Mean
Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,
More informationCS 1675 Introduction to Machine Learning Lecture 12 Support vector machines
CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More informationNumerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract
Numercal Smulatos of the Complex Moded Korteweg-de Vres Equato Thab R. Taha Computer Scece Departmet The Uversty of Georga Athes, GA 002 USA Tel 0-542-2911 e-mal thab@cs.uga.edu Abstract I ths paper mplemetatos
More informationTHE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp
THE PUBLISHIN HOUSE PROCEEDINS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/8, THE UNITS IN Stela Corelu ANDRONESCU Uversty of Pteşt, Deartmet of Mathematcs, Târgu Vale
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationUnique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen
Vol No : Joural of Facult of Egeerg & echolog JFE Pages 9- Uque Coo Fed Pot of Sequeces of Mags -Metrc Sace M. Ara * Noshee * Deartet of Matheatcs C Uverst Lahore Pasta. Eal: ara7@ahoo.co Deartet of Matheatcs
More informationAn Efficient Conjugate- Gradient Method With New Step-Size
Australa Joural of Basc a Ale Sceces, 5(8: 57-68, ISSN 99-878 A Effcet Cojugate- Graet Metho Wth New Ste-Sze Ba Ahma Mtras, Naa Faleh Hassa Deartmet of Mathematcs College of Comuters Sceces a Mathematcs
More informationSolutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17
Itroucto to Ecoometrcs (3 r Upate Eto) by James H. Stock a Mark W. Watso Solutos to O-Numbere E-of-Chapter Exercses: Chapter 7 (Ths erso August 7, 04) 05 Pearso Eucato, Ic. Stock/Watso - Itroucto to Ecoometrcs
More informationA conic cutting surface method for linear-quadraticsemidefinite
A coc cuttg surface method for lear-quadratcsemdefte programmg Mohammad R. Osoorouch Calfora State Uversty Sa Marcos Sa Marcos, CA Jot wor wth Joh E. Mtchell RPI July 3, 2008 Outle: Secod-order coe: defto
More informationQR Factorization and Singular Value Decomposition COS 323
QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?
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 informationADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLE
Joural o Mathematcs ad tatstcs 9 (3): 49-55, 03 I: 549-3644 03 cece Publcatos do:0.3844/jmssp.03.49.55 Publshed Ole 9 (3) 03 (http://www.thescpub.com/jmss.toc) ADAPTIVE CLUTER AMPLIG UIG AUXILIARY VARIABLE
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More informationFourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION
Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar
More informationBinary classification: Support Vector Machines
CS 57 Itroducto to AI Lecture 6 Bar classfcato: Support Vector Maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Supervsed learg Data: D { D, D,.., D} a set of eamples D, (,,,,,
More informationA Multistep Broyden s-type Method for Solving System of Nonlinear Equations
Joural of Novel Appled Sceces Avalable ole at www.jasc.org 7 JNAS Joural-7-6-/39-5 ISSN 3-549 7 JNAS A Multstep royde s-ype Method for Solvg System of Nolear Equatos M.Y.Wazr, M.A. Alyu ad A.Wal 3 - Departmet
More informationCSE 5526: Introduction to Neural Networks Linear Regression
CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto
More information0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING
CONVEX OPIMIZAION AND INERIOR POIN MEHODS FINAL PROJEC / INEGER PROGRAMMING AND SEMIDEFINE PROGRAMMING b Luca Buch ad Natala Vktorova CONENS:.Itroducto.Formulato.Applcato to Kapsack Problem 4.Cuttg Plaes
More informationBabatola, P.O Mathematical Sciences Department Federal University of Technology, PMB 704 Akure, Ondo State, Nigeria.
Iteratoal Joural o Matematcs a Statstcs Stues ol., No., Marc 0, pp.45-6 Publse b Europea Cetre or esearc, rag a Developmet, UK www.ea-jourals.org NUMEICAL SOLUION OF ODINAY DIFFEENIAL EQUAIONUSING WO-
More informationMinimization of Unconstrained Nonpolynomial Large-Scale Optimization Problems Using Conjugate Gradient Method Via Exact Line Search
Amerca Joural of Mechacal ad Materals Eeer 207; (): 0-4 http://wwwscecepublshroupcom/j/ajmme do: 0648/jajmme207003 Mmzato of Ucostraed Nopolyomal Lare-Scale Optmzato Problems Us Cojuate Gradet Method Va
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More information2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America
SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates
More informationChapter 5. Curve fitting
Chapter 5 Curve ttg Assgmet please use ecell Gve the data elow use least squares regresso to t a a straght le a power equato c a saturato-growthrate equato ad d a paraola. Fd the r value ad justy whch
More informationTHE PROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION
Joural of Scece ad Arts Year 12, No. 3(2), pp. 297-32, 212 ORIGINAL AER THE ROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION DOREL MIHET 1, CLAUDIA ZAHARIA 1 Mauscrpt receved: 3.6.212; Accepted
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationCan we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?
Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets
More informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationComplete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables
Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl
More informationLecture 12: Multilayer perceptrons II
Lecture : Multlayer perceptros II Bayes dscrmats ad MLPs he role of hdde uts A eample Itroducto to Patter Recoto Rcardo Guterrez-Osua Wrht State Uversty Bayes dscrmats ad MLPs ( As we have see throuhout
More informationGUARANTEED REAL ROOTS CONVERGENCE OF FITH ORDER POLYNOMIAL AND HIGHER by Farid A. Chouery 1, P.E. US Copyright 2006, 2007
GUARANTEED REAL ROOTS CONVERGENCE OF FITH ORDER POLYNOMIAL AND HIGHER by Fard A. Chouery, P.E. US Copyrght, 7 Itroducto: I dg the roots o the th order polyomal, we d the aalable terate algorthm do ot ge
More informationOn Signed Product Cordial Labeling
Appled Mathematcs 55-53 do:.436/am..6 Publshed Ole December (http://www.scrp.or/joural/am) O Sed Product Cordal Label Abstract Jayapal Baskar Babujee Shobaa Loaatha Departmet o Mathematcs Aa Uversty Chea
More informationEVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM
EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal
More information( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006
Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More informationOn Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros
It. Joural of Math. Aalyss, Vol. 7, 2013, o. 20, 983-988 HIKARI Ltd, www.m-hkar.com O Modfed Iterval Symmetrc Sgle-Step Procedure ISS2-5D for the Smultaeous Icluso of Polyomal Zeros 1 Nora Jamalud, 1 Masor
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationBERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler
Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,
More informationNon-uniform Turán-type problems
Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at
More informationUNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS
Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method
More informationSupport vector machines II
CS 75 Mache Learg Lecture Support vector maches II Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Learl separable classes Learl separable classes: here s a hperplae that separates trag staces th o error
More informationMaximum Walk Entropy Implies Walk Regularity
Maxmum Walk Etropy Imples Walk Regularty Eresto Estraa, a José. e la Peña Departmet of Mathematcs a Statstcs, Uversty of Strathclye, Glasgow G XH, U.K., CIMT, Guaajuato, Mexco BSTRCT: The oto of walk etropy
More informationh-analogue of Fibonacci Numbers
h-aalogue of Fboacc Numbers arxv:090.0038v [math-ph 30 Sep 009 H.B. Beaoum Prce Mohammad Uversty, Al-Khobar 395, Saud Araba Abstract I ths paper, we troduce the h-aalogue of Fboacc umbers for o-commutatve
More informationAPPLYING TRANSFORMATION CHARACTERISTICS TO SOLVE THE MULTI OBJECTIVE LINEAR FRACTIONAL PROGRAMMING PROBLEMS
Iteratoal Joural of Computer See & Iformato eholog IJCSI Vol 9, No, Aprl 07 APPLYING RANSFORMAION CHARACERISICS O SOLVE HE MULI OBJECIVE LINEAR FRACIONAL PROGRAMMING PROBLEMS We Pe Departmet of Busess
More informationStatistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018
/3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg
More informationCONTINUABILITY OF THE SOLUTIONS OF DIFFERENTIAL EQUATIONS WITH VARIABLE STRUCTURE AND UNLIMITED NUMBER OF IMPULSIVE EFFECTS
ТРАДИЦИИ ПОСОКИ ПРЕДИЗВИКАТЕЛСТВА Юбилейна национална научна конференция с международно участие Смолян 9 2 октомври 22 CONTINUABILITY OF THE SOLUTIONS OF DIFFERENTIAL EQUATIONS WITH VARIABLE STRUCTURE
More informationAnalysis of Variance with Weibull Data
Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad
More information1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.
PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg
More informationChapter 2 Supplemental Text Material
-. Models for the Data ad the t-test Chapter upplemetal Text Materal The model preseted the text, equato (-3) s more properl called a meas model. ce the mea s a locato parameter, ths tpe of model s also
More information1. Linear second-order circuits
ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationAnalyzing Two-Dimensional Data. Analyzing Two-Dimensional Data
/7/06 Aalzg Two-Dmesoal Data The most commo aaltcal measuremets volve the determato of a ukow cocetrato based o the respose of a aaltcal procedure (usuall strumetal). Such a measuremet requres calbrato,
More informationPROJECTION PROBLEM FOR REGULAR POLYGONS
Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c
More informationChapter 3. Differentiation 3.3 Differentiation Rules
3.3 Dfferetato Rules 1 Capter 3. Dfferetato 3.3 Dfferetato Rules Dervatve of a Costat Fucto. If f as te costat value f(x) = c, te f x = [c] = 0. x Proof. From te efto: f (x) f(x + ) f(x) o c c 0 = 0. QED
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 informationChapter 14 Logistic Regression Models
Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as
More informationQ-analogue of a Linear Transformation Preserving Log-concavity
Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com
More informationOn the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros
It. Joural of Math. Aalyss, Vol. 7, 2013, o. 59, 2947-2951 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.12988/ma.2013.310259 O the Iterval Zoro Symmetrc Sgle Step Procedure IZSS1-5D for the Smultaeous
More information