Homogenization of random Hamilton Jacobi Bellman Equations

Similar documents
Hamilton- J acobi Equation: Explicit Formulas In this lecture we try to apply the method of characteristics to the Hamilton-Jacobi equation: u t

Hamilton- J acobi Equation: Weak S olution We continue the study of the Hamilton-Jacobi equation:

Vanishing Viscosity Method. There are another instructive and perhaps more natural discontinuous solutions of the conservation law

An Introduction to Malliavin calculus and its applications

2. Nonlinear Conservation Law Equations

MATH 4330/5330, Fourier Analysis Section 6, Proof of Fourier s Theorem for Pointwise Convergence

Uniqueness of solutions to quadratic BSDEs. BSDEs with convex generators and unbounded terminal conditions

Hamilton Jacobi equations

Math 527 Lecture 6: Hamilton-Jacobi Equation: Explicit Formulas

THE WAVE EQUATION. part hand-in for week 9 b. Any dilation v(x, t) = u(λx, λt) of u(x, t) is also a solution (where λ is constant).

THE MYSTERY OF STOCHASTIC MECHANICS. Edward Nelson Department of Mathematics Princeton University

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance.

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Chapter 2. First Order Scalar Equations

Predator - Prey Model Trajectories and the nonlinear conservation law

Math 2214 Solution Test 1A Spring 2016

Physics 127b: Statistical Mechanics. Fokker-Planck Equation. Time Evolution

Solutions to Assignment 1

THE SINE INTEGRAL. x dt t

Math Final Exam Solutions

EXERCISES FOR SECTION 1.5

t 2 B F x,t n dsdt t u x,t dxdt

The Asymptotic Behavior of Nonoscillatory Solutions of Some Nonlinear Dynamic Equations on Time Scales

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n

arxiv: v1 [math.ca] 15 Nov 2016

u(x) = e x 2 y + 2 ) Integrate and solve for x (1 + x)y + y = cos x Answer: Divide both sides by 1 + x and solve for y. y = x y + cos x

Ends of the moduli space of Higgs bundles. Frederik Witt

Section 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3

10. State Space Methods

MA 366 Review - Test # 1

Tracking Adversarial Targets

Stochastic Model for Cancer Cell Growth through Single Forward Mutation

On a Fractional Stochastic Landau-Ginzburg Equation

Algorithmic Trading: Optimal Control PIMS Summer School

International Journal of Scientific & Engineering Research, Volume 4, Issue 10, October ISSN

Dual Representation as Stochastic Differential Games of Backward Stochastic Differential Equations and Dynamic Evaluations

6. Stochastic calculus with jump processes

Heavy Tails of Discounted Aggregate Claims in the Continuous-time Renewal Model

ON THE NUMBER OF FAMILIES OF BRANCHING PROCESSES WITH IMMIGRATION WITH FAMILY SIZES WITHIN RANDOM INTERVAL

14 Autoregressive Moving Average Models

4 Sequences of measurable functions

Oscillation of an Euler Cauchy Dynamic Equation S. Huff, G. Olumolode, N. Pennington, and A. Peterson

Omega-limit sets and bounded solutions

Ann. Funct. Anal. 2 (2011), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:

Properties Of Solutions To A Generalized Liénard Equation With Forcing Term

Lecture 10: The Poincaré Inequality in Euclidean space

arxiv:math-ph/ v1 1 Jan 1998

dy dx = xey (a) y(0) = 2 (b) y(1) = 2.5 SOLUTION: See next page

= ( ) ) or a system of differential equations with continuous parametrization (T = R

Convergence of the Neumann series in higher norms

Example on p. 157

1 st order ODE Initial Condition

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes

Differential Equations

Fractional Method of Characteristics for Fractional Partial Differential Equations

Finish reading Chapter 2 of Spivak, rereading earlier sections as necessary. handout and fill in some missing details!

Unit Root Time Series. Univariate random walk

Empirical Process Theory

Homework 10 (Stats 620, Winter 2017) Due Tuesday April 18, in class Questions are derived from problems in Stochastic Processes by S. Ross.

MATH 5720: Gradient Methods Hung Phan, UMass Lowell October 4, 2018

di Bernardo, M. (1995). A purely adaptive controller to synchronize and control chaotic systems.

Math 334 Test 1 KEY Spring 2010 Section: 001. Instructor: Scott Glasgow Dates: May 10 and 11.

Elements of Stochastic Processes Lecture II Hamid R. Rabiee

L p -L q -Time decay estimate for solution of the Cauchy problem for hyperbolic partial differential equations of linear thermoelasticity

Innova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*)

Ordinary Differential Equations

Chapter 6. Systems of First Order Linear Differential Equations

Solutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore

23.2. Representing Periodic Functions by Fourier Series. Introduction. Prerequisites. Learning Outcomes

Couplage du principe des grandes déviations et de l homogénisation dans le cas des EDP paraboliques: (le cas constant)

Some Basic Information about M-S-D Systems

Robust estimation based on the first- and third-moment restrictions of the power transformation model

Math 23 Spring Differential Equations. Final Exam Due Date: Tuesday, June 6, 5pm

Undetermined coefficients for local fractional differential equations

Lipschitz stability for a coefficient inverse problem for the non-stationary transport equation via Carleman estimate

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...

(1) (2) Differentiation of (1) and then substitution of (3) leads to. Therefore, we will simply consider the second-order linear system given by (4)

ME 391 Mechanical Engineering Analysis

Class Meeting # 10: Introduction to the Wave Equation

Two Coupled Oscillators / Normal Modes

dt = C exp (3 ln t 4 ). t 4 W = C exp ( ln(4 t) 3) = C(4 t) 3.

Some Regularity Properties of Three Dimensional Incompressible Magnetohydrodynamic Flows

Theory of! Partial Differential Equations!

The Quantum Theory of Atoms and Molecules: The Schrodinger equation. Hilary Term 2008 Dr Grant Ritchie

( ) a system of differential equations with continuous parametrization ( T = R + These look like, respectively:

Harnack inequalities and Gaussian estimates for a class of hypoelliptic operators

MA Study Guide #1

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is

Theory of! Partial Differential Equations-I!

CONTRIBUTION TO IMPULSIVE EQUATIONS

MA 214 Calculus IV (Spring 2016) Section 2. Homework Assignment 1 Solutions

12: AUTOREGRESSIVE AND MOVING AVERAGE PROCESSES IN DISCRETE TIME. Σ j =

1 1 + x 2 dx. tan 1 (2) = ] ] x 3. Solution: Recall that the given integral is improper because. x 3. 1 x 3. dx = lim dx.

A remark on the H -calculus

Echocardiography Project and Finite Fourier Series

Chapter 3 Boundary Value Problem

On Gronwall s Type Integral Inequalities with Singular Kernels

A Sharp Existence and Uniqueness Theorem for Linear Fuchsian Partial Differential Equations

Optima and Equilibria for Traffic Flow on a Network

Transcription:

Probabiliy, Geomery and Inegrable Sysems MSRI Publicaions Volume 55, 28 Homogenizaion of random Hamilon Jacobi Bellman Equaions S. R. SRINIVASA VARADHAN ABSTRACT. We consider nonlinear parabolic equaions of Hamilon Jacobi Bellman ype. The Lagrangian is assumed o be convex, bu wih a spaial dependence which is saionary and random. Rescaling in space and ime produces a similar equaion wih a rapidly varying spaial dependence and a small viscosiy erm. Moivaed by corresponding resuls for he linear ellipic equaion wih small viscosiy, we seek o find he limiing behavior of he soluion of he Cauchy (final value) problem in erms of a homogenized problem, described by a convex funcion of he gradien of he soluion. The main idea is o use he principle of dynamic programming o wrie a variaional formula for he soluion in erms of soluions of linear problems. We hen show ha asympoically i is enough o resric he opimizaion o a subclass, one for which he asympoic behavior can be fully analyzed. The paper oulines hese seps and refers o he recenly published work of Kosygina, Rezakhanlou and he auhor for full deails. Homogenizaion is a heory abou approximaing soluions of a differenial equaion wih rapidly varying coefficiens by a soluion of a consan coefficien differenial equaion of a similar naure. The simples example of is kind is he soluion u " of he equaion u " D x 2 a u " xx " I u".; x/ D f.x/ on Œ;. The funcion a. / is assumed o be uniformly posiive, coninuous and periodic of period. The limi u of u " exiss and solves he equaion u D Na 2 u xxi u.; x/ D f.x/ where Na is he harmonic mean dx Na D : a.x/ 397

398 S. R. SRINIVASA VARADHAN Alhough his is a resul abou soluions of PDE s i can be viewed as a limi heorem in probabiliy. If we consider he Markov process x./ wih generaor 2 a.x/d2 x saring from a ime, as! he limiing disribuion of y./ D x./ p Gaussian wih mean and variance Na. The acual variance of y./ is E a.x.s// ds : The resul on he convergence of u " o u is seen o follow from an ergodic heorem of he ype lim a.x.s// ds D Na:! From he heory of Markov processes one can see an ergodic heorem of his ype wih Na D a.x/.x/ dx; where.x/ is he normalized invarian measure on Œ; wih end poins idenified. This is seen o be dx.x/ D a.x/ a.x/ ; so ha dx Na D a.x/.x/ dx D : a.x/ We can consider he siuaion where a.x/ D a.x;!/ is a random process, saionary wih respec o ranslaions in x. We can formally consider a probabiliy space. ; ; P/, and an ergodic acion x of on. We also have a funcion a.!/ saisfying < c a.!/ C <. The saionary process a.x;!/ is given by a.x;!/ D a. x!/. Now he soluion u " of is u ".; x;!/ D 2 a.x;!/u" xx.; x;!/i u".; x;!/ D f.x/ can be shown o converge again, in probabiliy, o he nonrandom soluion u of u.; x/ D Na 2 u xx.; x/i u ".; x/ D f.x/ wih Na D a.!/ dp :

HOMOGENIATION OF RANDOM HAMILTON JACOBI BELLMAN EQUATIONS 399 This is also an ergodic heorem for a.!.s// ds; bu he acual Markov process!./ for which he ergodic heorem is proved is one ha akes values in wih generaor L D 2 a.!/d2 ; where D is he generaor of he ranslaion group x on. The invarian measure is seen o be dq D Na a.!/ dp; where Na D a.x/ dp : We will ry o adap his ype of approach o some nonlinear problems of Hamilon-Jacobi Bellman ype. One par of he work ha we ouline here was done joinly wih Elena Kosygina and Fraydoun Rezakhanlou and has appeared in prin [Kosygina e al. 26], while anoher par, carried ou wih Kosygina, has been submied for publicaion. The problems we wish o consider are of he form u " C " x 2 u" C H " ; ru" ;! D I u.t; x/ D f.x/ for Œ; T d. Here f is a coninuous funcion wih a mos linear growh.. ; ; P/ is a probabiliy space on which d acs ergodically as measure preserving ransformaions x. H.; p;!/ is a funcion on d which is a convex funcion of p for every! and H.x; p;!/ D H.; p; x!/. I saisfies some bounds and some addiional regulariy. The problem is o prove ha u "! u as "!, where u is a soluion of u C H.ru/ D I u.t; x/ D f.x/ for some convex funcion H.p/ of p and deermine i. The analysis consiss of several seps. We migh as well assume T D and concenrae on u ".; ;!/. Firs we noe ha, by rescaling, he problem can be reduced o he behavior of lim! u.; ;!/; where u is he soluion in Œ; d, of u s C 2u C H.x; ru;!/i u.; x/ D f x :

4 S. R. SRINIVASA VARADHAN The second sep is o use he principle of dynamic programming o wrie a variaional formula for u.s; x;!/. Denoe by L. x!; q/ he convex dual L.x; q;!/ D sup p hp; qi H.x; p;!/ Le b.s; x/ be a funcion b W Œ; d! d. Le B denoe he space all such bounded funcions. For each b 2 B, we consider he linear equaion vs b C 2 vb C hb.s; x/; rv b x i L. x!; b.s; x// D ; v.; x/ D f I hen he soluion u.s; x/ is sup b v b.s; x/. If we denoe by Q b he Markov process wih generaor L b s D 2 C hb.s; x/; ri saring from.; /, hen v b.; ;!/ D E Qb f. x./ / L.x.s/; b.s; x.s//;!/ ds and u D sup v b b2b The hird sep is o consider a subclass of B of he form b.; x/ D c. x!/ wih c W! d chosen from a reasonable class C. The soluion v b wih his choice of b.; x/ D b.x/ D c. x!/ will be denoed by v c. We will show ha for our choice of C, he limi lim! vc.; ;!/ D g.c/ will exis for every c 2 C. I hen follows ha lim inf! u.; / sup g.c/: c2c Given c here is a Markov process Q c;! on saring from! wih generaor A c D 2 C hc.!/; ri: Here r is he infiniesimal generaor of he d acion f x g and D r r. This process can be consruced by solving dx./ D c. x./!/ d C ˇ./I x./ D Then one lifs i o by defining!./ D x./!. Such a process wih generaor A c could have an invarian densiy P c and i could (alhough i is unlikely) be muually absoluely coninuous wih respec o P, having densiy c. c will be a weak soluion of 2 c D r c. / c:

HOMOGENIATION OF RANDOM HAMILTON JACOBI BELLMAN EQUATIONS 4 We can hen expec g.c/ D f c.!/ dp c L!; c.!/ dp c : In general he exisence of such a for a given c is nearly impossible o prove. On he oher hand for a given finding a c is easy. For insance, will do. More generally one can have c D r 2 c D r 2 C c ; so long as r c D. So pairs.c; / such ha 2 c D r c. / c exis. Our class C will be hose for which exiss. I is no hard o show, using he ergodiciy of f x g acion, ha is unique for a given c when i exiss and he Markov process wih generaor A c is ergodic wih dp c D cdp as invarian measure. We will denoe by C he class of pairs.c; / saisfying he above relaion. So we have a lower bound where lim inf! I.m/ D uc.; / sup m2 d Œf.m/ inf c; W.c; /2C R c dpdm I.m/ L.c.!/;!/ dp Now we urn o proving upper bounds. Fix 2 d. If we had a nice es funcion W.x;!/ such ha for almos all! jw.x;!/ h; xij o.jxj/ and 2W C H.x; rw;!/ Then, by convex dualiy wih QW D W.x;!/.s /, we have QW s C 2 QW C hb.s; x/; r QW i L.b.s; x/;!/ : If H./ is defined as H./ D inff W W exissg

42 S. R. SRINIVASA VARADHAN hen under some conrol on he growh of L, i is no hard o deduce ha wih f.x/ D h; xi, lim sup! u.; ;!/ H./ If we can prove ha H./ D sup m Œh; mi I.m/; we are done. We would mach he upper and lower bounds. We reduce his o a minmax equals maxmin heorem. sup m Œh; mi I.m/ D sup hc.!/; i L.c.!/;!/ dp.c; /2C D sup inf hc.!/; i C A c W L.c.!/;!/ dp.c; / W D inf hc.!/; i C A c W L.c.!/;!/ dp W.c; / D inf W 2 W C H. C rw;!/ dp D inf W sup! D H./: 2 W C H. C rw;!/ dp While W may no exis, rw will exis. We can inegrae on d, hen ergodic heorem will yield an esimae of he form W.x/ D o.jxj/ and h; xi C W.x/ will work as a es funcion. There are some echnical deails on he issues of growh and regulariy. The deails have appeared in [Kosygina e al. 26] along wih addiional references. Similar resuls on he homogenizaion of random Hamilon Jacobi Bellman equaions have been obained by Lions and Souganidis [25], using differen mehods. Now we examine he ime dependen case. If we replace d acion by dc acion wih.; x/ denoing ime and space, hen he saionary processes H and L are space ime processes. The lower bound works more or less in he same manner. In addiion o r we now have D he derivaive in he ime direcion. The!./ process is he space-ime process. Is consrucion for a given c is slighly differen. We sar wih b.; x/ D c. ;x!/ and consruc a diffusion on d corresponding o he ime dependen generaor A c s D 2 C hb.s; x/; ri

HOMOGENIATION OF RANDOM HAMILTON JACOBI BELLMAN EQUATIONS 43 and hen lif i by!.s/ D s;x.s/!. The invarian densiies are soluions of D C 2 D r c : The lower bound works he same way. Bu for obaining he upper bound, a es funcion W has o be consruced ha saisfies W C 2W C H.; x; rw;!/ H./ In he ime independen case here was a lower bound on he growh of he convex funcion H ha provided esimaes on rw. Here one has o work much harder in order o conrol in some manner W. The deails will appear in [Kosygina and Varadhan 28]. References [Kosygina and Varadhan 28] E. Kosygina and S. R. S. Varadhan, Homogenizaion of Hamilon Jacobi Bellman equaions wih respec o ime-space shifs in a saionary ergodic medium, Comm. Pure Appl. Mah. 6:6 (28). [Kosygina e al. 26] E. Kosygina, F. Rezakhanlou, and S. R. S. Varadhan, Sochasic homogenizaion of Hamilon Jacobi Bellman equaions, Comm. Pure Appl. Mah. 59: (26), 489 52. [Lions and Souganidis 25] P.-L. Lions and P. E. Souganidis, Homogenizaion of viscous Hamilon Jacobi equaions in saionary ergodic media, Comm. Parial Differenial Equaions 3:-3 (25), 335 375. S. R. SRINIVASA VARADHAN COURANT INSTITUTE NEW YORK UNIVERSITY 25 MERCER STREET NEW YORK, NY 2 UNITED STATES varadhan@cims.nyu.edu