L-BFGS and Delayed Dynamical Systems Approach for Unconstrained Optimization

transcript

Xiaohui XIE

Supervisor: Dr. Hon Wah TAM

Outline Problem background and introduction Analysis for dynamical systems with time delay

Introduction of dynamical systems Delayed dynamical systems approach Uniqueness property of dynamical systems

Numerical testing Comparison between L-BFGS and steepest descent

method A new code Radar 5

Main stages of this research APPENDIX

1. Problem background and introduction

Optimization problems are classified into four parts, our research is focusing on unconstrained optimization problems.

1min : nf x f R Rnx R

Steepest descent method

For (UP), is a descent direction at

or is a descent

direction for .

0Tf x p

/ xfxfp f x

Method of Steepest Descent

Find that solves Then

Unfortunately, the steepest descent method converges only linearly, and sometimes very slowly linearly.

k .min0 kk xfxf

1 .k k k kx x f x

Newton’s method

Newton’s direction— Newton’s method Given , compute

Although Newton’s method converges very fast, the Hessian matrix is difficult to compute.

kk xfxf 12

1 ,k k k kx x f x f x

Quasi-Newton method—BFGS

Instead of using the Hessian matrix, the quasi-Newton methods approximate it.

In quasi-Newton methods, the inverse of the Hessian matrix is approximated in each iteration by a positive definite (p.d.) matrix, say .

being symmetric and p.d. implies the descent property.

k k kp H f x

The most important quasi-Newton formula— BFGS.

where THEOREM 1 If is a p.d. matrix, and , then in (2) is also positive definite.(Hint: we can write , and let and )

BFGSk ys

syHHysysss

BFGSkH 0k

BFGSkH 1

TkH LL Ta L z T

kb L y

kkk xxs 1 kkkkk ggxfxfy 11

Limited-Memory Quasi-Newton Methods —L-BFGS

Limited-memory quasi-Newton methods are useful for solving large problems whose Hessian matrices cannot be computed at a reasonable cost or are not sparse.

Various limited-memory methods have been proposed; we focus mainly on an algorithm known as L-BFGS.

Tkkkkk

Tkk ssVHVH 1

kk syIV

kkk xxs 1 kkk ffy 1

The L-BFGS approximation satisfies the following formula:

for (7)

mk 11 1 0 0 0 1

1 0 0 0 1

1 2 2 2 1

T T Tk k k k k

T T Tk k

T T Tk k k k k k k

T Tk k k k k

Tk k k

H V V V H V V V

V V s s V V

V s s V

mk 1 1 1 1 0 1 1

2 1 1 1 2

1 2 2 2 1

T T Tk k k k m k m k k

T T Tk k m k m k m k m k m k

T T Tk k k k k k k

T Tk k k k k

Tk k k

H V V V H V V V

V V s s V V

V s s V

2. Analysis for dynamical systems with time delay

The unconstrained problem (UP) is reproduced. (8)

It is very important that the optimization problem is posted in the continuous form, i.e. x can be changed continuously.

The conventional methods are addressed in the discrete form.

1min :n

x Rf x f R R

Dynamical system approach

The essence of this approach is to convert (UP) into a dynamical system or an ordinary differential equation (o.d.e.) so that the solution of this problem corresponds to a stable equilibrium point of this dynamical system.

Consider the following simple dynamical system or ode Neural network approach

The mathematical representation of neural network is an ordinary differential equation which is asymptotically stable at any isolated solution point.

xpdttdx

Some Dynamical system versions

Based on the steepest descent direction

Based on the Newton’s direction

Other dynamical systems

dx f x tdt

12dx tf x t f x t

dx ts t p x t

d x t dx ta t b t B x t p x t

Delayed dynamical systems approach Dynamical system approach can solve very large

problems. How to find a “good” ?

steepest descent direction

slow convergence

Newton’s direction

difficult to compute

fast convergence and easy to calculate

The delayed dynamical systems approach solves the delayed o.d.e. (13)

For , we use

To compute at .

,( ( ), ( ( )), ..., ( ( ))) ( )1dx t

H x t x t t x t t f x tmdt

1 0 1 1 0

1 2 1 1 2 0 1 0 0 1 1 2 2 1 1

1 2 1 1 2 0 1 0 1 0 1 1 2 2 1 1

1 2 1 2 1 2 1 1

, , , : , , , ,

T T T Tm m m m m m

T Tm m m m m m m m

Tm m m

H x t x t x t H x t x t x t x t

V t V t V t V t H V t V t V t V t

V t V t V t t s t s t V t V t V t

V t t s t s t V t

t s t s t

1 1 1 1

1 1 1 111 1

m m m m

Tm m m mT m

s t x t x t y t f x t f x t

t V t I t y t s ty t s t

Beyond this point we save only m previous values of x. The definition of H is now, for m k,

2 1 1 2 1

1 2 3 1 2 0 1 2 2 3 1

1 2 3 1 2 1 2 1 2 2

, , , , : , , , ,

k k m k m k k k m k m

T T T Tk k k k m k m k m k m k m k m k m k m k k k

T T T Tk k k k m k m k m k m k m k m k m k m k m

H x t x t x t x t H x t x t x t x t

V t V t V t V t H V t V t V t V t

V t V t V t t s t s t V

k m k k k

T Tk k k k k k k k

Tk k k

t V t V t

V t t s t s t V t

t s t s t

k k k k

Tk k k kT k

s t x t x t y t f x t f x t

t V t I t y t s ty t s t

Uniqueness property of dynamical systems

2121 )()( xxLxFxF

Lipschitz continuity

,)(),()(),( 1 uuLufwuHufwuH

.)(),()(),( 2 wwLufwuHufwuH

3. Numerical testing

Test problems1. Extended Rosenbrock function2. Penalty function Ⅰ3. Variable dimensioned function4. Linear function-rank 1

Result of modified Rosenbrock problem

t value stepL-BFGS 2 0 497

Steepest descent 23.2813 0.0006 53557

Comparison of function value

Comparison of norm of gradient

A new code — Radar 5

The code RADAR5 is for stiff problems, including differential-algebraic and neutral delay equations with constant or state-dependent (eventually vanishing) delays.

1'( ) ( , ( ), ( ( , ( ))), , ( ( , ( ))))mMy t f t y t y t t y t y t t y t

0 0 0( ) , ( ) ( )y t y y t g t for t t

Breaking points Discontinuities occur in different orders of the derivative of the

solutions To detect the breaking point----- find the value of t to make the

function zero is a previous breaking point and a suitable continuous

approximation to the solution.

Implicit Runge-Kutta method

Delay differential equation Radar 5

( ) ( , ( ))d t t u t ( )u

Theorem 3.1

Consider the DDE

where is -continuous in , the initial function is -continuous and the delay is -continuous in . Moreover, assume that the mesh includes all the discontinuity points of order lying in . If the underlying CRK method has discrete order and uniform order , then the DDE method has discrete global order and uniform global order ; that is and ,where .

( , , )f t y x pC 0[ , ] d dft t R R

( , )t y 0[ , ] dft t R

0 1{ , , , , }n N ft t t t t p 0[ , ]ft t

1max ( ) ( )q

n nn Ny t y h

'max ( ) ( ) ( )f

t t ty t t h

1max n N nh h

0'( ) ( , ( ), ( ( , ( )))),

( ) ( )fy t f t y t y t t y t t t t

p q' min{ , 1}q p q

4. Main stages of this research

Prove that the function H in (13) is positive definite. (APPENDIX)

Prove that H is Lipschitz continuous. Show that the solution to (13) is asymptotically stable. Show that (13) has a better rate of convergence than the

dynamical system based on the steepest descent direction.

Perform numerical testing. Apply this new optimization method to practical

problems.

APPENDIX To show that H in (13) is positive definite

Property 1. If is positive definite, the matrix defined by (13) is positive definite (provided for all ).

I proved this result by induction. Since the continuous

analog of the L-BFGS formula has two cases, the proof needs to cater for each of them.

Ti sy i

When , is p.d. (Theorem 1) Assume that is p.d. when

1m 1kH

1 1 1 0 1 1 2 1 1 1 2

3 2 2 2 3 1 2 2 2 1

l T T T T T Tk k k k l k l k k k k l k l k l k l k l k

T T T T T Tk k l k l k l k l k l k k k k k k k k

T T Tk k k k k k k k

H V V V H V V V V V s s V V

V V s s V V V V s s V V

V s s V s s

11 1 1 0 1 1 .l T T T T T

k k k k l k l k l k l k l k l k l k kH V V V V H V s s V V V

In this case there is no exists.

By the assumption is p.d., it is obvious that is also p.d..

k k k k k k kH V H V s s

L-BFGS and Delayed Dynamical Systems Approach for Unconstrained Optimization

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