Variational Methods & Optimal Control

Transcription

1 Variational Methods & Optimal Control lecture 02 Matthew Roughan Discipline of Applied Mathematics School of Mathematical Sciences University of Adelaide April 14, 2016 Variational Methods & Optimal Control: lecture 02 p.1/29

2 Revision, part ii Extrema of functions of multiple variables. Taylor s theorem and the chain rule in N-D. Hessians and classification of extrema. Variational Methods & Optimal Control: lecture 02 p.2/29

3 Extrema of functions of one variable Local extrema have f (x)=0 includes maxima, minima, and stationary points of inflection sin 2 (x) x Variational Methods & Optimal Control: lecture 02 p.3/29

4 Classification of extrema Local extrema have f (x)=0 f (x)>0 local minima f (x)<0 local maxima f (x)=0 it might be a stationary point of inflection, depending on higher order derivatives, e.g. x 4. local max stationary point local min Variational Methods & Optimal Control: lecture 02 p.4/29

5 Functions of n variables Let Ω be a closed region of IR n, i.e. Ω IR n Let x=(x 1,x 2,...,x n ) Ω Let f : Ω IR A local minima if f( ) is point x such that there exists δ>0 where f(ˆx) f(x) for any ˆx B(x;δ). A global minima of f( ) on Ω is point x such that f(ˆx) f(x) for any ˆx Ω. Variational Methods & Optimal Control: lecture 02 p.5/29

6 2D example 1 f(x 1,x 2 )=x 2 1 x2 2 + x3 1 local maximum at( 2/3,0) saddle point at(0,0) Variational Methods & Optimal Control: lecture 02 p.6/29

7 2D example 2 f(x 1,x 2 )=r 1/2r 2, where r= x x2 2 global maxima on curve r= 1 local minima at r=0 Variational Methods & Optimal Control: lecture 02 p.7/29

8 2D example 3 f(x 1,x 2 )=x 3 2 3x2 1 x 2 Monkey saddle at(0,0) Variational Methods & Optimal Control: lecture 02 p.8/29

9 The Chain rule The derivative of a function f(x 1,x 2 ) along a line described parametrically by(x 1 (t),x 2 (t)) d f = f dx 1 x 1 + f dx 2 x 2 Another way to think of this is as the directional derivative formed from the dot product of grad and the direction of the line, e.g., d f = f dx ( f =, f ) x 1 x 2 ( dx1, dx ) 2 Variational Methods & Optimal Control: lecture 02 p.9/29

10 Chain Rule for more variables The chain rule (for a function of more than one variable) f(x)= f(x 1,x 2,...,x n ), where we want to find the derivative of a function f(x) along a line described parametrically by(x 1 (t),x 2 (t),...,x n (t)) then we take or alternatively df = d f = f dx 1 x 1 + f dx 2 x 2 f dx n + + x n ( f, f,..., f ) (. dx1 x 1 x 2 x n, dx 2,..., dx ) n = f. dx Variational Methods & Optimal Control: lecture 02 p.10/29

11 A graphical example For a function of two variables f(x,y) we get 3 d f = f x dx + f y dy f(x,y) = x+y x = cost y = sint z y Variational Methods x & Optimal Control: lecture 02 p.11/29

12 A graphical example For a function of two variables f(x,y) we get d f = f x dx + f y dy f(x,y) = x+y x = cost y = sint z df y Variational Methods x & Optimal Control: lecture 02 p.11/29

13 A graphical example For a function of two variables f(x,y) we get d f = f x dx + f y dy f(x,y) = x+y x = cost y = sint z df dx dy df y Variational Methods x & Optimal Control: lecture 02 p.11/29

14 Chain Rule Derivation By the definition df = lim ε 0 f(x(t+ ε),y(t+ ε)) f(x(t),y(t)) ε But note that from Taylor s theorem x(t+ ε)=x(t)+εx (t)+o(ε 2 ). As we consider the limit as ε 0 we may ignore the O(ε 2 ) term, to get df = lim ε 0 f(x(t)+εx (t),y(t)+εy (t)) f(x(t),y(t)) ε Variational Methods & Optimal Control: lecture 02 p.12/29

15 Chain Rule Derivation df f(x(t)+εx (t),y(t)+εy (t)) f(x(t),y(t)) = lim ε 0 ε f(x(t)+εx (t),y(t)+εy (t)) f(x(t),y(t)+εy (t)) = lim ε 0 ε f(x(t),y(t)+εy (t)) f(x(t),y(t)) +lim ε 0 ε = x f(x(t)+εx (t),y(t)+εy (t)) f(x(t),y(t)+εy (t)) (t)lim ε 0 εx (t) +y (t)lim ε 0 f(x(t),y(t)+εy (t)) f(x(t),y(t)) εy (t) Variational Methods & Optimal Control: lecture 02 p.13/29

16 Chain Rule Derivation df = x (t)lim ε 0 f(x(t)+εx (t),y(t)+εy (t)) f(x(t),y(t)+εy (t)) εx (t) = x (t) lim εx 0 +y (t)lim ε 0 f(x(t),y(t)+εy (t)) f(x(t),y(t)) εy (t) +y (t) lim εy 0 = x (t) f x + y (t) f y f(x(t)+ε x,y(t)+εy (t)) f(x(t),y(t)+εy (t)) ε x f(x(t),y(t)+ε y ) f(x(t),y(t)) ε y which is the chain rule! Variational Methods & Optimal Control: lecture 02 p.14/29

17 Chain Rule special case When we only have one variable, we simple want to calculate the derivative of a function f of another function x, e.g. d f(x(t))= df dx dx Another way of writing this is d f(x(t))= f [x(t)]x (t), which is the form you learnt in 1st year. Variational Methods & Optimal Control: lecture 02 p.15/29

18 Taylor s theorem in 2D f f f(x 1 + δx 1,x 2 + δx 2 )= f(x 1,x 2 )+δx 1 + δx 2 x 1 x [ δx 2 2 f 2 f 1 2 x δx 1 δx 2 + δx 2 2 ] f 2 x 1 x 2 x2 2 + Write(δx 1,δx 2 )=ε (η 1,η 2 ) f(x+εη)= f(x)+ε ( f f η 1 + η 2 x 1 x 2 + ε2 2 [ η f x 2 1 ) 2 f + 2η 1 η 2 + η 2 2 f 2 x 1 x 2 x2 2 ] + O(ε 3 ) Variational Methods & Optimal Control: lecture 02 p.16/29

19 Taylor s theorem in N-D f(x+δx)= f(x)+ n f δx i + 1 i=1 x i 2 n i, j=1 2 f x i x j δx i δx j + O(δx 3 ) f(x+δx)= f(x)+δx T f(x)+ 1 2 δxt H(x)δx+O(δx 3 ) Where H(x) is the Hessian matrix H(x)= 2 f x f x 2 x 1. 2 f x n x 1 2 f x 1 x 2 2 f x f x n x 2 2 f x 1 x n 2 f x 2 x n. 2 f x 2 n Variational Methods & Optimal Control: lecture 02 p.17/29

20 Maxima of N variables If a smooth function f(x) has a local extrema at x then f(x)= ( f, f,..., f ) T = 0 x 1 x 2 x n A sufficient condition for the extrema x to be a local minimum is for the quadratic form Q(δx 1,...,δx n )=δx T H(x)δx= to be strictly positive definite. n i=1 n j=1 2 f x i x j δx i δx j Variational Methods & Optimal Control: lecture 02 p.18/29

21 Quadratic forms A quadratic form Q(x)= a i j x i x j = x T Ax i, j is said to be positive definite if Q(x)>0 for all x 0. A quadratic form is positive definite iff every eigenvalue of A is greater than zero. A quadratic form is positive definite if all the principal minors in the top-left corner of A are positive, in other words a 11 a a 11 a 12 a a 11 > 0, a 21 a 22 > 0, a 21 a 22 a 23 > 0, a 31 a 32 a 33 Variational Methods & Optimal Control: lecture 02 p.19/29

22 Notes on maxima and minima maxima of f(x) are minima of f(x). haven t said anything about non-differentiable functions if continuous in the interval, must achieve maximum (minimum) in the interval Variational Methods & Optimal Control: lecture 02 p.20/29

23 Calculus of variations We are not maximizing the value of a function. We are maximizing a functional a function of a function Can think of it as an -dimensional max. problem. can choose between different functions function sits in -dimensional vector space This might take some effort. Variational Methods & Optimal Control: lecture 02 p.21/29

24 Functionals A Functional maps an element of a vector space (e.g. a space containing functions) to a real number, e.g. F : S IR. Example Functionals F{y(x)} = y(0) F{y(x)} = max{y(x)} x F{y(x)} = dy dx x=1 F{y(x)} = y(0)+y(1) F{y(x)} = N a n y(n) n=0 Variational Methods & Optimal Control: lecture 02 p.22/29

25 Integral functionals Previous functionals not very interesting. Easy to find y(x) which minimizes these. Integral functionals are more interesting. Example integral functionals F{y} = F{y} = F{y} = b a b a b a y(x)dx f(x)y(x)dx 1+ ( ) 2 dy dx dx Variational Methods & Optimal Control: lecture 02 p.23/29

26 Some simple cases F{y} = df dε If a and b are fixed then b(ε) a(ε) y(x, ε)dx = y(b,ε) db dε y(a,ε)da dε + b(ε) a(ε) y(x, ε) dx ε da dε db dε = 0 = 0 and so the derivative of the integral becomes the integral of the derivative. Variational Methods & Optimal Control: lecture 02 p.24/29

27 Crude Brachystochrone Brachystochrone involves the functional F{y}= x1 x 0 1+y 2 Let us guess that the brachystochrone takes the form y y(x,ε)=(1 x) ε We could calculate the derivative WRT ε as above and compute the stationary points by finding df dε = 0 dx Variational Methods & Optimal Control: lecture 02 p.25/29

28 Crude Brachystochrone 1.5 (1 x), total time = 2.00 wire velocity 1.5 (1 x). 4, total time = (1 x). 25, total time = wire velocity 1 wire velocity x x x Variational Methods & Optimal Control: lecture 02 p.26/29

29 Crude Brachystochrone 2 descent time ε = ε but what if the family of curves doesn t contain the maximum? Variational Methods & Optimal Control: lecture 02 p.27/29

30 Extra bits Variational Methods & Optimal Control: lecture 02 p.28/29

31 Notation f(x) : S IR denotes a function that maps the set S IR n to a real number. n f x n i denotes the nth partial derivative of f(x), with respect to x i. the ε-neighborhood under the Euclidean norm is B(x;ε)={ ˆx IR n ˆx x 2 < ε} The Euclidean norm in IR n is x 2 = n i=1 x 2 i F{y} denotes a functional of the function y(x). Variational Methods & Optimal Control: lecture 02 p.29/29