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### Review

Paul Heckbert

Computer Science Department

Carnegie Mellon University

15-859B - Introduction to Scientific Computing

y’=y

15-859B - Introduction to Scientific Computing

y’=-y, stable but slow solution

15-859B - Introduction to Scientific Computing

y’=-y, unstable solution

15-859B - Introduction to Scientific Computing

Jacobian of ODE

- ODE: y’=f(t,y), where y is n-dimensional
- Jacobian of f is a square matrix
- if ODE homogeneous and linear then J is constant and y’=Jy
- but in general J varies with t and y

15-859B - Introduction to Scientific Computing

Stability of ODE depends on Jacobian

At a given (t,y) find J(t,y) and its eigenvalues

find rmax = maxi { Re[i(J)] }

if rmax<0, ODE stable, locally

rmax =0, ODE neutrally stable, locally

rmax >0, ODE unstable, locally

15-859B - Introduction to Scientific Computing

Stability of Numerical Solution

- Stability of numerical solution is related to, but not the same as stability of ODE!
- Amplification factor of a numerical solution is the factor by which global error grows or shrinks each iteration.

15-859B - Introduction to Scientific Computing

Stability of Euler’s Method

- Amplification factor of Euler’s method is I+hJ
- Note that it depends on h and, in general, on t & y.
- Stability of Euler’s method is determined by eigenvalues of I+hJ
- spectral radius (I+hJ)= maxi | i(I+hJ) |
- if (I+hJ)<1 then Euler’s method stable
- if all eigenvalues of hJ lie inside unit circle centered at –1, E.M. is stable
- scalar case: 0<|hJ|<2 iff stable, so choose h < 2/|J|

- What if one eigenvalue of J is much larger than the others?

15-859B - Introduction to Scientific Computing

Implicit Methods

- use information from future time tk+1 to take a step from tk
- Euler method: yk+1 = yk+f(tk,yk)hk
- backward Euler method: yk+1 = yk+f(tk+1,yk+1)hk
- example:
- y’=Ay f(t,y)=Ay
- yk+1 = yk+Ayk+1hk
- (I-hkA)yk+1=yk -- solve this system each iteration

15-859B - Introduction to Scientific Computing

Stability of Backward Euler’s Method

- Amplification factor of B.E.M. is (I-hJ)-1
- B.E.M. is stable independent of h (unconditionally stable) as long as rmax<0, i.e. as long as ODE is stable
- Implicit methods such as this permit bigger steps to be taken (larger h)

15-859B - Introduction to Scientific Computing

Large steps OK with Backward Euler’s method

15-859B - Introduction to Scientific Computing

Popular IVP Solution Methods

- Euler’s method, 1st order
- backward Euler’s method, 1st order
- trapezoid method (a.k.a. 2nd order Adams-Moulton)
- 4th order Runge-Kutta
- If a method is pth order accurate then its global error is O(hp)

15-859B - Introduction to Scientific Computing

Solving Boundary Value Problems

- Shooting Method
- transform BVP into IVP and root-finding

- Finite Difference Method
- transform BVP into system of equations (linear?)

- Finite Element Method
- choice of basis: linear, quadratic, ...
- collocation method
- residual is zero at selected points

- Galerkin method
- residual function is orthogonal to each basis function

15-859B - Introduction to Scientific Computing

2nd Order PDE Classification

- We classify conic curve ax2+bxy+cy2+dx+ey+f=0 as ellipse/parabola/hyperbola according to sign of discriminant b2-4ac.
- Similarly we classify 2nd order PDE auxx+buxy+cuyy+dux+euy+fu+g=0:
b2-4ac < 0 – elliptic (e.g. equilibrium)

b2-4ac = 0 – parabolic (e.g. diffusion)

b2-4ac > 0 – hyperbolic (e.g. wave motion)

For general PDE’s, class can change from point to point

15-859B - Introduction to Scientific Computing

Example: Wave Equation

- utt = c uxx for 0x1, t0
- initial cond.: u(0,x)=sin(x)+x+2, ut(0,x)=4sin(2x)
- boundary cond.: u(t,0) = 2, u(t,1)=3
- c=1
- unknown: u(t,x)
- simulated using Euler’s method in t
- discretize unknown function:

15-859B - Introduction to Scientific Computing

k

k-1

j+1

j

j-1

Wave Equation: Numerical Solutionu0 = ...

u1 = ...

for t = 2*dt:dt:endt

u2(2:n) = 2*u1(2:n)-u0(2:n)

+c*(dt/dx)^2*(u1(3:n+1)-2*u1(2:n)+u1(1:n-1));

u0 = u1;

u1 = u2;

end

15-859B - Introduction to Scientific Computing

Wave Equation Results

15-859B - Introduction to Scientific Computing

Poor results when dt too big

dx=.05

dt=.06

Euler’s method unstable when step too large

15-859B - Introduction to Scientific Computing

PDE Solution Methods

- Discretize in space, transform into system of IVP’s
- Discretize in space and time, finite difference method.
- Discretize in space and time, finite element method.
- Latter methods yield sparse systems.

- Sometimes the geometry and boundary conditions are simple (e.g. rectangular grid);
- Sometimes they’re not (need mesh of triangles).

15-859B - Introduction to Scientific Computing

PDE’s and Sparse Systems

- A system of equations is sparse when there are few nonzero coefficients, e.g. O(n) nonzeros in an nxn matrix.
- Partial Differential Equations generally yield sparse systems of equations.
- Integral Equations generally yield dense (non-sparse) systems of equations.
- Sparse systems come from other sources besides PDE’s.

15-859B - Introduction to Scientific Computing

Example: PDE Yielding Sparse System

- Laplace’s Equation in 2-D: 2u = uxx +uyy = 0
- domain is unit square [0,1]2
- value of function u(x,y) specified on boundary
- solve for u(x,y) in interior

15-859B - Introduction to Scientific Computing

Sparse Matrix Storage

- Brute force: store nxn array, O(n2) memory
- but most of that is zeros – wasted space (and time)!

- Better: use data structure that stores only the nonzeros
col 1 2 3 4 5 6 7 8 9 10...

val 0 1 0 0 1 -4 1 0 0 1...

16 bit integer indices: 2, 5, 6, 7,10

32 bit floats: 1, 1,-4, 1, 1

- Memory requirements, if kn nonzeros:
- brute force: 4n2 bytes, sparse data struc: 6kn bytes

15-859B - Introduction to Scientific Computing

An Iterative Method: Gauss-Seidel

- System of equations Ax=b
- Solve ith equation for xi:
- Pseudocode:
until x stops changing

for i = 1 to n

x[i] (b[i]-sum{ji}{a[i,j]*x[j]})/a[i,i]

- modified x values are fed back in immediately
- converges if A is symmetric positive definite

15-859B - Introduction to Scientific Computing

Conjugate Gradient Method

- Generally for symmetric positive definite, only.
- Convert linear system Ax=b
- into optimization problem: minimize xTAx-xTb
- a parabolic bowl

- Like gradient descent
- but search in conjugate directions
- not in gradient direction, to avoid zigzag problem

- Big help when bowl is elongated (cond(A) large)

15-859B - Introduction to Scientific Computing

Convergence ofConjugate Gradient Method

- If K = cond(A) = max(A)/ min(A)
- then conjugate gradient method converges linearly with coefficient (sqrt(K)-1)/(sqrt(K)+1) worst case.
- often does much better: without roundoff error, if A has m distinct eigenvalues, converges in m iterations!

15-859B - Introduction to Scientific Computing

Example: Poisson’s Equation

- Sweep of Gauss-Seidel “relaxes” each grid value to be the average of its four neighbors plus an f offset
- Many relaxations required to solve this on a fixed grid.
- Multigrid solves it on a hierarchy of grids.

15-859B - Introduction to Scientific Computing

Elements of Multigrid Method

- relax on a given grid a few times
- coarsen (restrict) a grid
- refine (interpolate) a grid

15-859B - Introduction to Scientific Computing

finest grid kk

k/2k/2

...

coarsest grid 11

time

=relax

=interpolate

=restrict

A Common Multigrid ScheduleFull Multigrid V Cycle:

15-859B - Introduction to Scientific Computing

Some Iterative Methods

- Gauss-Seidel
- converges for all symmetric positive definite A

- Conjugate Gradient (CG) Method
- convergence rate determined by condition number
- note that condition number typically larger for finer grids

- Preconditioned Conjugate Gradient
- instead of solving Av=f, solve M-1Av=M-1f where M-1 is cheap and M is close to A
- often much faster than CG, but conditioner M is problem-dependent

- Multigrid
- convergence rate is independent of condition number, problem size
- but algorithm must be tuned for a given problem; not as general as others
note: don’t need matrix A in memory – can compute it on the fly!

15-859B - Introduction to Scientific Computing

Cost Comparison

on 2-D Poisson Equation, kk grid, n=k2 unknowns

METHOD COST

Gaussian Elimination O(k6) = O(n3)

Gauss-Seidel O(k4logk) = O(n2logn)

Conjugate Gradient O(k3) = O(n1.5)

FFT/cyclic reduction O(k2logk) = O(nlogn)

multigrid O(k2) = O(n)

optimal!

15-859B - Introduction to Scientific Computing

Function Representations

- sequence of samples (time domain)
- finite difference method

- pyramid (hierarchical)
- polynomial
- sinusoids of various frequency (frequency domain)
- Fourier series

- piecewise polynomials (finite support)
- finite element method, splines

- wavelet (hierarchical, finite support)
- (time/frequency domain)

15-859B - Introduction to Scientific Computing

What Are Wavelets?

In general, a family of representations using:

- hierarchical (nested) basis functions
- finite (“compact”) support
- basis functions often orthogonal
- fast transforms, often linear-time

15-859B - Introduction to Scientific Computing

Nested Function Spaces for Haar Basis

- Let Vj denote the space of all piecewise-constant functions represented over 2j equal sub-intervals of [0,1]
- Vj has basis

15-859B - Introduction to Scientific Computing

Function Representations –Desirable Properties

- generality – approximate anything well
- discontinuities, nonperiodicity, ...

- adaptable to application
- audio, pictures, flow field, terrain data, ...

- compact – approximate function with few coefficients
- facilitates compression, storage, transmission

- fast to compute with
- differential/integral operators are sparse in this basis
- Convert n-sample function to representation in O(nlogn) or O(n) time

15-859B - Introduction to Scientific Computing

Time-Frequency Analysis

- For many applications, you want to analyze a function in both time and frequency
- Analogous to a musical score
- Fourier transforms give you frequency information, smearing time.
- Samples of a function give you temporal information, smearing frequency.
- Note: substitute “space” for “time” for pictures.

15-859B - Introduction to Scientific Computing

Comparison to Fourier Analysis

- Fourier analysis
- Basis is global
- Sinusoids with frequencies in arithmetic progression

- Short-time Fourier Transform (& Gabor filters)
- Basis is local
- Sinusoid times Gaussian
- Fixed-width Gaussian “window”

- Wavelet
- Basis is local
- Frequencies in geometric progression
- Basis has constant shape independent of scale

15-859B - Introduction to Scientific Computing

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