mathematical · 2019. 2. 12. · [ 1), and kolmorogov and fomin [ 1). the martingale and...

T R A N S L A T I O N S O F

MATHEMATICAL M O N O G R A P H S V O L U M E 4 7

M. B. Nevelson R. Z. Has'minskff

Stochastic Approximation and Recursive Estimation

fl| American Mathematical Society Providence, Rhode Island

10.1090/mmono/047

CTOXACTMMECKAH AnnPOKCMMAUMf l

M PEKYPPEKTHOE OUEHMBAHM E

M. B. HEBEJlbCOH , P . 3 . XACbMMHCKM W

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2000 Mathematics Subject Classification. Primar y 62L20 ; Secondary 60J05 , 93E10 .

Abstract. Thi s boo k i s devote d t o sequentia l method s o f solvin g a

c lass o f problem s t o whic h belongs , fo r example , th e proble m o f findin g a

maximum poin t o f a functio n i f eac h measure d valu e o f thi s functio n con -

tains a rando m error . Som e basi c procedure s o f stochasti c approximatio n

are investigate d fro m a singl e poin t o f view , namel y th e theor y o f Marko v

processes an d martingales . Example s ar e considere d o f application s o f

the theorem s t o som e problem s o f estimatio n theory , educationa l theor y an d

control theory , an d als o t o som e problem s o f informatio n transmissio n i n

the presenc e o f invers e feedback .

Library of Congress Cataloging in Publication Data w ^m ay

Nevel'son, Mikhai l Borisovich . Stochast ic approximatio n an d recursiv e estimation .

(Translations o f mathematica l monograph s ; v . ^7 ) Translat ion o f Stokhasticheskai a approksimatfe£ 3

i rekurrentnoe oftenivanie. Bibliography: p. Includes index . 1. Stochast i c approximation . 2 . Estimatio n

theory. I . Kha s 'minskii, Rafai l Zalmanovich , jo in t author. H . T i t l e . I I I . Ser ies . QA27lf.NWl5 519.2 76-U8298 ISBN 0-8218-1597-0

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TABLE OF CONTENTS Preface 1 Introduction 4 Chapter 1 . Element s of probability and martingales 1 1

1. Probabilit y 1 1 2. Rando m variables. 1 3 3. Conditiona l probabilities and conditional expectations 1 7 4. Independence . Produc t measures 2 0 5. Martingale s and supermartingales 2 2

Chapter 2. Discret e time Markov processes 2 9 1. Marko v processes 2 9 2. Marko v processes and supermartingales 3 2 3. Recursivel y define d processe s 3 4 4. Discret e model of diffusion . 3 7 5. Exi t of sample functions fro m a domain 3 8 6. Serie s of independent rando m variables 4 1 7. Convergenc e of sample functions 4 3 8. Teachin g pattern recognition 4 9

Chapter 3. Marko v processes and stochastic equations 5 3 1. Continuou s time Markov processes 5 3 2. Stochasti c differential equations . 1 5 7 3. Stochasti c integrals 5 9 4. Stochasti c differential equations . II 6 2 5. Ito' s formula 6 4 6. Supermartingale s 6 9 7. Existenc e of solutions in the large 7 0 8. Exi t from a domain. Convergenc e of sample functions 7 3

Chapter 4. Convergenc e of stochastic approximation procedures . 1 7 9 1. Th e Robbins-Monro procedure 7 9 2. Th e Kiefer-Wolfowitz procedur e 8 3 3. Continuou s procedures 8 6 4. Convergenc e of the Robbins-Monro procedure 8 8 5. Convergenc e of the Kiefer-Wolfowitz procedur e 9 4

ui

CONTENTS

Chapter 5. Convergenc e o f stochasti c approximatio n procedures . I I 10 1 1. Introductor y remark s 10 1 2. Genera l theorems 10 2 3. Auxiliar y result s (continuous time ) 10 6 4. Auxiliar y result s (discrete time ) 11 2 5. One-dimensiona l procedure s 11 8

Chapter 6. Asymptoti c normalit y o f the Robbins-Monr o procedure 12 3 1. Preliminar y remark s 12 3 2. Asymptoti c behavior of solution s 12 9 3. Investigatio n o f the process ^(r) . 13 2 4. Investigatio n o f th e process I2{f) 13 6 5. Asymptoti c normalit y (continuou s time) 14 0 6. Asymptoti c normalit y (discrete time ) 14 7 7. Convergenc e o f moments 15 4

Chapter 7 . Som e modifications o f stochasti c approximatio n procedure s 16 1 1. Statemen t o f th e proble m 16 1 2. Genera l theore m 16 2 3. Auxiliar y result s 16 5 4. Theorem s on convergence and asymptotic normality 16 7 5. Adaptiv e Robbins-Monr o procedure s 16 9 6. Asymptoti c optimalit y 17 5

Chapter 8 . Recursiv e estimatio n (discrete time ) 18 1 1. Th e Crame'r-Rao inequality. Efficienc y o f estimate s 18 1 2. Th e Cramer-Rao inequality i n the multidimensiona l cas e 18 6 3. Estimatio n of a one-dimensional paramete r 18 9 4. Asymptoticall y efficien t recursiv e procedure 19 4 5. Estimatio n of a multidimensional parameter 19 7 6. Estimatio n with dependent observation s 20 2

Chapter 9. Recursiv e estimation (continuous time ) 20 7 1. Th e Cramer-Ra o inequalit y 20 7 2. Applicatio n o f th e Robbins-Monro procedur e 21 0 3. Time-dependen t observation s 21 2 4. Som e applications 21 3 5. A modification 22 0

Chapter 10 . Recursiv e estimation with a control parameter 22 1 1. Statemen t o f th e problem 22 1 2. Asymptoticall y optima l recursiv e plan 22 3 3. Tw o examples... . 22 6 4. Continuou s case 22 9

Notes on the literatur e 23 3 Bibliography 23 7 Subject Index 24 2 Main Notation 24 4

IV

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NOTES ON THE LITERATURE

Chapter 1

The proo f o f al l theorem s cite d i n § § 1 - 4 ma y b e foun d i n Kolmogoro v [ 1 ] , Halmos

[ 1), an d Kolmorogo v an d Fomi n [ 1). Th e martingal e an d supermartingale concept s ar e du e

to Doob . Theorem s S. l an d 5.l ' an d thei r proof s ar e take n fro m Doo b [ 1 ] .

Chapter 2

§ 1. Fo r mor e detail s abou t propertie s o f Marko v processes an d thei r transitio n func -

tions, see Felle r [ 1 ], Dynkin [ 1 ], Loev e [ 1J, an d others .

§2. Theore m 2. 1 i s essentiall y a special cas e o f Doob' s theore m (Doo b [1 ) ) o n th e

stopping o f a supermartingale. Kolomogorov' s inequalit y fo r supermartingale s ma y als o b e

found i n Doo b [ 1 ].

§3. Theorem s 3. 1 an d 3. 2 ar e wel l known . See , fo r example , Gihman an d Skoroho d

HI. §5. Fo r related results , see, fo r example , AB R [ 1 ] , Chapter IV .

§6. Theorem s 6. 1 an d 6. 2 wer e prove d b y Kolomogoro v [1 ] i n a more genera l setting .

§7. Theore m 7. 1 i s conceptuall y simila r t o Theore m 8 i n Chapte r 4 o f AB R [ 1 ] .

§ 8. Fo r a detailed discussio n o f th e question s relate d t o thi s formulatio n o f th e prob -

lem, see AB R [ 1 ], Chapter 5 .

Chapter 3

§ 1. Fo r mor e detail s abou t th e definitio n an d propertie s o f a continuous Marko v

process, see Dynki n [ 1 ].

§2. Th e limi t passag e fro m equatio n (2.1 ) t o a continuou s Marko v process was con -

sidered b y Bernstei n [ 1 ], who, among othe r things , prove d a theorem o n th e limi t behavio r

of th e transitio n probabilit y a s h -* 0 .

§§3 an d 4 . Thi s definitio n o f th e stochasti c integra l i s du e t o ItS . Fo r a proo f o f

Theorem 4.1 , see, fo r example , Gihma n an d Skorohod [ 2 ] .

§7. A proo f o f Theore m 7. 1 ma y b e foun d i n Has'minski T [ 1 ] .

§8. Simila r results ar e presente d in th e authors ' paper [ 1 ] .

Chapter 4

§ 1. A s mentione d i n th e text , s.a . procedure s fo r locatio n o f th e root s o f regressio n

equations wer e first suggeste d i n th e 19S 1 pape r o f Robbin s an d Monr o [ 1 ] , who prove d

convergence i n mea n squar e o f th e procedur e (S.l ) unde r certai n assumptions . Thi s resul t

233

234 NOTES O N TH E LITERATUR E

was generalize d b y Wolfowit z [ 1 ], Blu m [ 1J, Chun g [ 1 ], an d others . Blu m [ 1 ), i n particular ,

presented th e firs t proo f o f convergenc e wit h probabilit y 1 . Theore m 1. 1 an d th e metho d o f

proof use d i n th e tex t ar e du e t o Gladyse v [ 1 ]. Multidimensiona l R M procedure s wer e intro -

duced b y Blu m [ 2 ] . Blu m was th e firs t autho r t o appl y th e theor y o f supermartingale s t o

investigate th e convergenc e o f s.a . procedures .

§2. Th e procedure (2.5 ) wa s propose d b y Kiefe r an d Wolfowit z [1 ] i n 1952 . Blu m

[ 2 ] , Derma n [ 1 ] , Burkholde r [1 ] an d other s studie d th e procedur e i n detai l and , i n particular ,

proved tha t i t i s convergen t wit h probabilit y 1 . N o accoun t i s given her e o f mor e genera l s.a .

procedures suc h a s those propose d i n Burkholde r [ 1 ] , Dvoretzk y [ 1 ] , etc . Mor e detaile d

bibliographies ma y b e foun d i n th e revie w article s o f Dvoretzk y [ 1 ] , Fabia n [ 3 ] , Sakriso n

[ 3 ], Schmettere r [ 1J, Logino v [ 1 ], an d others .

§ 3. Th e continuou s analo g o f th e R M procedur e wa s introduced b y Drim l an d Nedom a

[ 1 ] . The y prove d th e convergenc e o f th e procedur e unde r fairl y genera l assumption s o n th e

stochastic processe s appearin g on th e righ t o f th e equation . Unde r certain assumptions ,

Sakrison [ 1 ] prove d th e convergenc e o f th e continuou s analog of th e Kiefer-Wolfowit z pro -

cedure. Th e continuou s procedure s considere d her e wer e define d i n Has'rninsk u [1 ] an d

Nevel'son an d Has'minsk u [ 1 ].

§4. Th e firs t convergenc e theorem s fo r multidimensiona l R M procedures wer e prove d

by Blu m [ 2 ] ; see als o ABR [ 1 ] , Schmetterer [1 ] an d furthe r bibliograph y cite d there . Theo -

rem 4. 4 i s similar t o th e above-mentione d resul t o f Gladyse v [ 1 ]; Theore m 4. 5 i s du e t o

Braverman an d Rozonoe r (se e ABR [ 1 ]). Th e formulatio n o f th e las t theore m raise s th e

question a s to whethe r on e ca n als o weaken th e conditio n Lfl 2(f) < « > in th e proo f tha t

X(t) - + XQ &J . I t turn s ou t tha t thi s i s indee d possible . Fo r example , i n som e case s i t i s

sufficient t o deman d tha t £o"(f ) < °° for som e n > 0 (althoug h on e mus t the n impos e mor e

stringent condition s o n th e "noise") . Th e theorem s prove d her e fo r continuou s R M proced -

ures ar e du e t o th e author s [ 1 ] .

§ 5. Convergenc e theorem s fo r discret e multidimensiona l K W procedures wer e prove d

by Blu m [ 2 ] , Dupa£ [1 ] an d others. Fo r th e continuou s case , see Nevel'so n an d Has'minsk u

[ 1 ] .

Chapter 5

The conjectur e tha t a K W procedure canno t converg e wit h positiv e probabilit y t o

minimum point s o f th e regressio n functio n wa s advance d b y Fabia n [ 1 J, [ 2 ] . Th e result s

presented her e ar e based o n Has'minsk u (1 ] an d Nevel'son [ 1 ] , [ 2 ] . Th e tw o last-mentione d

papers als o prov e mor e genera l theorems. Se e als o Krasulin a [ 1 ] .

Chapter 6

§1 . Theore m 1. 1 fo r th e one-dimensiona l cas e i s proved , e.g. , i n Loev e [ 1 ]; for th e

multidimensional case , see Sack s [ 1 ].

§2. Condition s fo r th e validit y o f th e estimat e EX 2{t) = 0 ( l / f ) a s t - * » , wher e X(f)

is a discret e one-dimensiona l R M process , were firs t obtaine d b y Chun g [ 1 ]. A n analo g o f

Lemma 2. 1 fo r discret e multidimensiona l R M processes was prove d b y Sack s [ 1 ] unde r th e

additional assumptio n E\G(t, x, t j ) | 2 < c < « . Th e fac t tha t (2.9 ) i s a consequence o f (2.8 )

follows fro m a well-known lemm a of Chun g [ 1 ] .

§§3 an d 4 . Lemm a 3. 1 wa s prove d b y Has'minski f [ 2 ] . Lemm a 4. 3 i s du e t o Sack s

NOTES O N TH E LITERATUR E 235

§5. Theore m 5. 3 fo r M > 0 i s prove d i n Has'minski T [ 2 ] . Th e asymptoti c normalit y

of a R M procedure wa s established , unde r certai n assumptions , b y Holev o [ 1 ].

§6. Theore m 6. 1 wa s prove d b y Sack s under th e followin g additiona l assumptions :

a) th e matri x B ma y b e reduced t o diagona l for m b y a similarity transformatio n involvin g

an orthogona l matrix ; b ) th e nor m o f th e matri x A(t, x) i s bounde d fo r t > 1 an d x € Ej.

Theorem 6. 3 i s new. Asymptoti c normalit y theorem s fo r othe r s.a . procedure s wer e prove d

by Fabia n [ 5 ] , Derman [ 1 ] , Burkhoide r [ 1 ] , DupaS [1 ] an d others . Th e "truncation "

method i n asymptoti c normalit y proof s fo r s.a . procedure s was firs t applie d b y Hodge s an d

Lehman [ 1 ] .

§ 7. Th e firs t convergenc e theorem s fo r moment s o f s.a . processe s wer e prove d b y

Chung [ 1 ] i n th e one-dimensiona l case . Fo r the multidimensiona l case , the resul t o f Theore m

7.2 i s wel l know n (see , fo r example , Schmettere r [ 1J) fo r X > *ta . Bu t i f Conditio n (a ) o f

§ 7 hold s with A < Via, the onl y resul t know n t o u s o n convergenc e o f moment s i s tha t o f

Sakrison [ 2 ] . Constructin g a certai n modificatio n o f th e R M procedure , Sakrison prove d

that th e matri x o f secon d moment s o f th e proces s *Ji(X{i) — XQ) converges t o th e correspon -

ding covarianc e matri x o f a normal law , provided al l moment s E|G(f , x, (J))P t p = 1 , 2 , . . . ,

are bounded .

Chapter 7

§ § 1 - 4 . Alber t an d Gardne r [1 ] establishe d convergenc e an d asymptoti c normalit y

for th e procedur e (1.1 ) an d a more genera l on e i n whic h th e facto r a{t) i s allowe d t o depen d

on pas t observations . Thes e author s als o considere d a multidimensional modificatio n o f (1.1) .

Recently, Cslb i [ 1 ] , [2 ] ha s prove d convergenc e o f truncate d procedure s fo r a broad rang e

of truncations .

§5. Ther e i s a considerable literatur e o n th e optima l choic e o f parameter s fo r R M

procedures an d thei r adaptiv e modifications . Amon g others , we mentio n Chun g [ 1 ],

Dvoretzky [ 1 ] , Keste n [ 1 ] , Cypkin [ 1 ] , [2],an d Stratonovic ' [ 2 ] . Ou r approac h i s base d

on th e wor k o f Vente r [ 1 ] , who prove d Theore m 5. 1 unde r slightly mor e restrictiv e assump -

tions.

§6. Theore m 6. 1 slightl y generalize s a result o f Vente r [ 1 ] ; Theorem 6. 2 i s new .

Chapter 8

§1. Theore m 1. 1 follow s fro m result s o f Chapma n an d Robbin s [1 ] an d Kaga n [ 1 ] .

Theorem 1. 2 wa s prove d b y Ibragimo v and Has'minski i [ 2 ] . Thes e paper s als o deriv e inequal -

ities fo r biase d estimates .

§2. Theore m 2. 1 an d it s analo g fo r biase d estimate s wer e prove d b y Ibragimo v an d

Has'minskii [ 2 ] .

§ 3. Th e us e o f R M procedure s fo r parametri c estimatio n i s discusse d i n Alber t an d

Gardner [ 1 ], wher e a larger clas s o f estimates , no t necessaril y formin g Marko v processes , i s

considered.

§§4 an d 5 . Theorem s 4. 1 an d 4. 5 wer e prove d b y Sakriso n [ 2 ] , [3 ] unde r stronge r

conditions (an d without th e convergenc e o f distributions) . Othe r optimalit y criteri a wer e

considered b y Alber t an d Gardne r [ 1 ] , Cypkin [ 1 ] , and Stratonovic ' [ 2 ] , but thei r procedure s

generally depen d o n pas t observation s (see , fo r example , th e procedur e (0.9 ) i n th e Intro -

duction).

236 NOTES ON THE LITERATURE

Chapter 9

§1. Fo r inequality (1.7) , see, for example, Van Trees [1]. §2. A procedure equivalent to (2.4) for m linear in the parameter (m(*0) = mxg ,

where m is a nonsingular matrix) was considered by Holevo [1] . H e also considered the case that m is not a square matrix but mm* is nonsingular. Fo r the nonlinear case, Holevo proposed a procedure more complicated than (2.4). Unde r certain assumptions, he established asymptotic normality o f the estimation procedure.

§3. Recursiv e procedures similar to (3.1) ma y be derived from the Bayesian approach, using linearization. Fo r linear systems, similar estimation procedures may be found in Kal-man and Bucy [1] , Lipcer and Sirjaev [1] , and elsewhere.

§4. Th e problem of estimating x0 base d on observations of type (4.7) (estimation o f linear regression coefficients) ha s been considered from other points of view in many papers.

§5. Wit h suitable initial conditions, the procedure (5.3) , (5.4) for a linear function m{t, x) = m{t)x coincide s with the Kalman-Buc y optimal linear filtering equations . I n other words, this system is satisfied, in particular, by the conditional expectation of XQ given Y(s), s < f , provide d the a priori distribution of XQ is Gaussian.

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SUBJECT INDE X

Admissible control , 22 1 asymptotically optimal , 23 0

Asymptotic efficiency, 18 6 normality, 12 3 variance, 18 5

Borel set , 1 2 Chapman-Kolmogorov relation , 3 0 Condition (A) , 3 6

(B), 7 2 (a), 15 5

Control parameter , 22 1 Convergence

almost sure , 1 5 in distribution , 1 5 in probability , 1 5

Diffusion coefficient, 3 8 matrix, 6 7

Distribution o f a vector, 1 4 Drift

coefficient, 3 8 vector, 6 7

Estimate, 18 1 asymptotically efficient , 186 , 21 0

strongly, 185 , 188 , 20 9 weakly, 186 , 18 8

asymptotically unbiased , 18 2 efficient, 185 , 18 8 maximum likelihood , 5 recursive, 190 , 21 0 strongly consistent , 18 2 unbiased, 18 2 weakly consistent , 18 2

Expectation, 1 5 conditional, 17 , 1 8

relative t o a random vector , 1 9 finite, 1 5

Fisher information , 18 2 matrix, 18 6

Function Borel, 1 3 characteristic (o f a set), 1 7 distribution, 1 4 Ljapunov, 8 9 measurable, 1 3

Gaussian whit e noise , 6 3 Generating operator , 3 1

differential, 6 7 Independent

events, 2 0 random variable an d a-algebra , 2 0 sets o f rando m vectors , 2 0 0-algebras, 20 vectors, 2 0

Inequality Cebysev's, 1 5 CrameV-Rao (information) , 18 2 Kolmogorov's (fo r supermartingales) , 33 -3 4

Information content, 6 , 18 2 Fisher, 18 2 inequality, 18 2 matrix (Fisher) , 18 6

Integral, Lebesgue , 1 4 Itd's formula fo r stochasti c differentials , 66—6 7 Kiefer-Wolfowitz procedure , 83ff . Lebesgue integral , 1 4 Lemma

Fatou's, 1 6 Ljapunov's, 13 3

Martingale, 22 , 23 , 6 9 Measurable space , 1 2 Measure, 1 2

absolutely continuous , 1 6 complete, 1 2 finite, 12 Lebesgue, 1 2 probability, 1 3 a-finlte, 1 2

Observation process , 20 7 Plan o f experiment , 22 1

asymptotically optimal , 22 3 Probability, conditional , 17 , 1 8

relative t o a random vector , 1 9 Probability space , 1 3 Product

of measures , 2 1 of spaces , 2 0

Random variable, 1 3 independent o f th e future , 6 1 vector-valued, 1 3

242

SUBJECT INDE X 243

Recursive procedure , 19 0 truncated, 16 2

Regularity condition , 7 1 Regularization, 3 1 Robbins-Monro procedure , 4 , 79ff . Sample functio n o f a stochastic process , Separating hype r plane, 5 0 Set o f elementar y events , 1 2 o-algebras, 1 2

Borel, 1 2 generated b y a random vector , 1 9 imbedding o f on e i n another , 1 2 intersection of , 1 2 minimal, 1 2

Signalling rate , 21 6 Space o f elementar y events , 11 , 1 2 Stable

matrix, 13 2 point, 10 6

Standard Wiene r process , 5 5 with value s i n £ j , 5 6

Statistic, 18 1 Stochastic

approximation procedure s 4 , 79ff . Kiefer-Wolfowitz, 83ff . Robbins-Monro, 4 , 79ff .

differential (Ito) , 6 3 equivalence, 1 4

of stochasti c processes , 5 4 strong, 6 2

integral (Ito's) , 6 0 22, 5 3 process , 5 3

discrete, 2 2 Markov, 30 , 5 4

homogeneous, 5 4 measurable, 5 3 regular, 7 1 separable, 5 3 stationary (i n th e narro w sense) , 14 4

Supermartingale, 23 , 6 9 Theorem

Fubini's, 2 1 Ito's, 6 6 Kolmogorov's, 5 6 Lebesgue's, 1 6 Radon-Nikodym, 1 6

Transition densit y o f a Markov process , 5 4 Transition function , 30 , 5 4

homogeneous, 3 1 temporally homogeneous , 5 4

Transition probabilit y o f a Markov process , 2 9

MAIN NOTATIO N

11*11 = yfcijbff- nor m of matri x B = (tyy)) , p . 12 5

C2: clas s o f function s V(t, x) continuousl y differentiabl e wit h respec t t o t an d twic e con -

tinuously differentiabl e wit h respec t t o x, p . 5 7

C2' se t o f function s V(x) € C 2 wit h bounde d second-orde r partia l derivatives , p . 9 2

Dr: domai n o f definitio n o f generatin g operator , p . 3 1

D£ ' x ' : domai n o f definitio n o f generatin g operato r L a t poin t (t, x), p . 3 1

Eji euclidea n /-space , p . 1 2

7;: monoton e famil y o f a*lgebras , pp . 36 , 5 9

/(x): Fishe r informatio n matrix , pp . 182 , 18 6

/ : identit y matrix , p . 12 4

L: generatin g operator , pp . 31 , 6 7

1*2fo b]: se t o f measurabl e rando m function s /(f , u>) , t € [a, b], a ; e ft, ^-measurabl e fo r

every f , suc h tha t / £ / (f , w)d f < « • with probabilit y 1 , p . 5 9

)lf: a-algebr a o f event s generate d b y rando m variable s X(u), u < t, pp . 30 , 7 3

U: closur e o f se t U, p . 16 2

U€(B): e-neighborhoo d o f se t B , p . 3 9

U€.RW = K €<*> n <* : W < * * ' P - 4 0

K€(B) = EJ\U €{B), p . 3 9

1: domai n o f value s o f unknow n paramete r x, p . 18 1

X(t) ~9l(m , 5) : asymptoti c normalit y o f X(t) 9 p . 12 3

*W = <y x y w ) ,P-202 2: domai n o f value s o f contro l paramete r z , p . 22 1 Vg/Cx): vector wit h coordinate s \f(x + ce/ ) - / ( x — ce/)]/2c, p . 8 6

dv/bx: matri x wit h element s dty/dxy , p . 10 7

r^: firs t exi t tim e fro m domai n G , pp . 32 , 7 3

4>(B): clas s o f function s define d o n p . 4 0

244