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Computational Physics (Lecture 7) PowerPoint Presentation

Computational Physics (Lecture 7)

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### Computational Physics(Lecture 7)

PHY4370

Quadratic form

- A quadratic form is simply a scalar, quadratic function of a vector with the form
- A is the matrix, x and b are vectors, c is a scalar constant. If A is symmetric and positive definite, f(x) is minimized by the solution to Ax=b.

- Sample Problem:
- c= 0

The gradient

- ,…,=-b.
- If A is symmetric, the equation becomes:
Ax-b.

Revisit of Steepest descent

- We start from an arbitrary starting point: x(0),
- Slide down to the bottom of the paraboloid.
- When we take the step, we choose the direction in which f decreases most quickly.
- -f’(x(i))=b-Ax(i).

- Error: e(i) = x(i)-x => how far from the solution.
- Residual: r(i) = b-Ax(i) => how far from the correct value of b.

- Suppose we start from (-2, -2). Along the steepest descent, will fall somewhere on the solid line.
- X(1) = x(0)+αr(0)
- How big the step we should take?
- Line search
- Choose α to minimize f. df(x(1))/dα=0,
- So, f’(x(1))Tr(0)=0
- So α is chosen to make r(0) and f’(x(1)) orthogonal!

Determine will fall somewhere on the solid line. α

- F(x(1))= -r(1)
- r(1)Tr(0) = 0
- (b-Ax(1))Tr(0) = 0
- (b-A(x(0)+αr(0)))Tr(0) = 0
- (b-Ax(0))Tr(0) = α(Ar(0))Tr(0)
- r(0) Tr(0)=αr(0)T(Ar(0))
- α =(r(0) Tr(0))/(r(0)TAr(0))

General method of Steepest Descent will fall somewhere on the solid line.

Conjugate directions will fall somewhere on the solid line.

- SD often takes steps in the same direction!
- If we take n orthogonal steps, each step has the correct length, afte n steps, we are done！
- In general, for each step, we have
- x(i+1)=x(i)+α(i)d(i)
- e(i+1) should be orthogonal to d(i)
- d(i)Te(i+1) = 0
- d(i)T (e(i)+ α(i)d(i))=0
- α(i)=-d(i) Te(i)/(d(i) Td(i))

- Useless! We don’t know e( will fall somewhere on the solid line. i).
- Instead, we make d(i) and d(j) A-orthogonal, or conjugate.
- d(i)TAd(j) = 0

- New requirement: will fall somewhere on the solid line.
- e(i+1) is A-orthogonal to d(i)
- Like SD, we need to find the minimum along d(i)

- This time, evaluate the will fall somewhere on the solid line. α(i) again,

Conjugate Gradients will fall somewhere on the solid line.

- Just the Conjugate directions method where the search directions are constructed by the conjugation of the residuals.
- ui=r(i)

General Method will fall somewhere on the solid line.

Sample code for SD will fall somewhere on the solid line.

Multi variable Newton method functions

- f(x) = 0, (f = ( f1, f2, . . . , fn) and x = (x1, x2, . . . , xn).)
- Taylor expansion at Xr:
- f(xr) = f(x) + x ·∇f(x) + O(x2) ≃0,
- AΔx = b,
- Aij= ∂ fi(x)/∂xj
- Bi=-fi

- For Newtonian method:
- Xk+1 = xk+ Δxk
- Secant Method

- Aij= (fi(x + hj<xj>) − fi(x))/hj
- hj≃δ0 xj
- δ0 isthe tolerance of the floating point of data.

- f1(x, y) = exp(x^2) lny − x^2 = 0 and f2(x, y) = exp(y2) lnx − y^2 = 0,
around x = y = 1.5.

// An example of searching for the root via the secant method

// for f_i[x_k] for i=1,2,...,n.

import java.lang.*;

public class Rootm {

public static void main(String argv[]) {

int n = 2;

intni = 10;

double del = 1e-6;

double x[] = {1.5, 1.5};

secantm(ni, x, del);

// Output the root obtained

System.out.println("The root is at x = " + x[0]

+ "; y = " + x[1]);

}

// Method to carry out the multivariable secant search.

public static void secantm(intni, double x[],

double del) {

int n = x.length;

double h = 2e-5;

int index[] = new int[n];

double a[][] = new double[n][n];

int k = 0;

double dx = 0.1;

while ((Math.abs(dx)>del) && (k<ni)) {

double b[] = f(x);

for (int i=0; i<n; ++i) {

for (int j=0; j<n; ++j) {

double hx = x[j]*h;

x[j] += hx;

double c[] = f(x);

a[i][j] = (c[i]-b[i])/hx;

}

}

dx = Math.sqrt(dx/n);

k++;

}

if (k==n) System.out.println("Convergence not" +

" found after " + ni + " iterations");

}

public static double[] solve(double a[][], double b[],

int index[]) {...}

public static void gaussian(double a[][], int index[])

{...}

// Method to provide function f_i[x_k].

public static double[] f(double x[]) {

double fx[] = new double[2];

fx[0] = Math.exp(x[0]*x[0])*Math.log(x[1])

-x[0]*x[0];

fx[1] = Math.exp(x[1])*Math.log(x[0])-x[1]*x[1];

return fx;

}

}

Extremes of a multi variable function, BFBG method

- f(x) = ∇g(x) = 0,

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