gradient descent method

Posted 2012.09.17 23:15

1. f(x)가 다음과 같이 주어졌을 때 

function [cost] = func_PotentialFunction( x0, param )


cost = norm[  x0(1)-param.x1center , x0(2)-param.x2center ];


2. f(x)의 Jacobian Matrix는 다음과 같이 구할 수 있다. 

param.x1center = 10;

param.x2center = 20;

 f = @(x) func_PotentialFunction ( x,  param  );


x0 = [5, 5];

[jac, err] = jacobianest(f, x0);


3. Jacobian estimation 함수 

function [jac,err] = jacobianest(fun, x0)

% gradest: estimate of the Jacobian matrix of a vector valued function of n variables

% usage: [jac,err] = jacobianest(fun,x0)

%

% arguments: (input)

%  fun - (vector valued) analytical function to differentiate.

%        fun must be a function of the vector or array x0.

%  x0  - vector location at which to differentiate fun

%        If x0 is an nxm array, then fun is assumed to be

%        a function of n*m variables.

%

%

% arguments: (output)

%  jac - array of first partial derivatives of fun.

%        Assuming that x0 is a vector of length p

%        and fun returns a vector of length n, then

%        jac will be an array of size (n,p)

%

%  err - vector of error estimates corresponding to

%        each partial derivative in jac.

%

%

% Example: (nonlinear least squares)

%  xdata = (0:.1:1)';

%  ydata = 1+2*exp(0.75*xdata);

%  fun = @(c) ((c(1)+c(2)*exp(c(3)*xdata)) - ydata).^2;

%

%  [jac,err] = jacobianest(fun,[1 1 1])

%

%  jac =

%           -2           -2            0

%      -2.1012      -2.3222     -0.23222

%      -2.2045      -2.6926     -0.53852

%      -2.3096      -3.1176     -0.93528

%      -2.4158      -3.6039      -1.4416

%      -2.5225      -4.1589      -2.0795

%       -2.629      -4.7904      -2.8742

%      -2.7343      -5.5063      -3.8544

%      -2.8374      -6.3147      -5.0518

%      -2.9369      -7.2237      -6.5013

%      -3.0314      -8.2403      -8.2403

%

%  err =

%   5.0134e-15   5.0134e-15            0

%   5.0134e-15            0   2.8211e-14

%   5.0134e-15   8.6834e-15   1.5804e-14

%            0     7.09e-15   3.8227e-13

%   5.0134e-15   5.0134e-15   7.5201e-15

%   5.0134e-15   1.0027e-14   2.9233e-14

%   5.0134e-15            0   6.0585e-13

%   5.0134e-15   1.0027e-14   7.2673e-13

%   5.0134e-15   1.0027e-14   3.0495e-13

%   5.0134e-15   1.0027e-14   3.1707e-14

%   5.0134e-15   2.0053e-14   1.4013e-12

%

%  (At [1 2 0.75], jac should be numerically zero)

%

%

% See also: derivest, gradient, gradest

%

%

% Author: John D'Errico

% e-mail: woodchips@rochester.rr.com

% Release: 1.0

% Release date: 3/6/2007


% get the length of x0 for the size of jac

nx = numel(x0);


MaxStep = 100;

StepRatio = 2.0000001;


% was a string supplied?

if ischar(fun)

  fun = str2func(fun);

end


% get fun at the center point

f0 = fun(x0);

f0 = f0(:);

n = length(f0);

if n==0

  % empty begets empty

  jac = zeros(0,nx);

  err = jac;

  return

end


relativedelta = MaxStep*StepRatio .^(0:-1:-25);

nsteps = length(relativedelta);


% total number of derivatives we will need to take

jac = zeros(n,nx);

err = jac;

for i = 1:nx

  x0_i = x0(i);

  if x0_i ~= 0

    delta = x0_i*relativedelta;

  else

    delta = relativedelta;

  end

  

  % evaluate at each step, centered around x0_i

  % difference to give a second order estimate

  fdel = zeros(n,nsteps);

  for j = 1:nsteps

    fdif = fun(swapelement(x0,i,x0_i + delta(j))) - ...

      fun(swapelement(x0,i,x0_i - delta(j)));

    

    fdel(:,j) = fdif(:);

  end

  

  % these are pure second order estimates of the

  % first derivative, for each trial delta.

  derest = fdel.*repmat(0.5 ./ delta,n,1);

  

  % The error term on these estimates has a second order

  % component, but also some 4th and 6th order terms in it.

  % Use Romberg exrapolation to improve the estimates to

  % 6th order, as well as to provide the error estimate.

  

  % loop here, as rombextrap coupled with the trimming

  % will get complicated otherwise.

  for j = 1:n

    [der_romb,errest] = rombextrap(StepRatio,derest(j,:),[2 4]);

    

    % trim off 3 estimates at each end of the scale

    nest = length(der_romb);

    trim = [1:3, nest+(-2:0)];

    [der_romb,tags] = sort(der_romb);

    der_romb(trim) = [];

    tags(trim) = [];

    

    errest = errest(tags);

    

    % now pick the estimate with the lowest predicted error

    [err(j,i),ind] = min(errest);

    jac(j,i) = der_romb(ind);

  end

end


end % mainline function end


% =======================================

%      sub-functions

% =======================================

function vec = swapelement(vec,ind,val)

% swaps val as element ind, into the vector vec

vec(ind) = val;


end % sub-function end


% ============================================

% subfunction - romberg extrapolation

% ============================================

function [der_romb,errest] = rombextrap(StepRatio,der_init,rombexpon)

% do romberg extrapolation for each estimate

%

%  StepRatio - Ratio decrease in step

%  der_init - initial derivative estimates

%  rombexpon - higher order terms to cancel using the romberg step

%

%  der_romb - derivative estimates returned

%  errest - error estimates

%  amp - noise amplification factor due to the romberg step


srinv = 1/StepRatio;


% do nothing if no romberg terms

nexpon = length(rombexpon);

rmat = ones(nexpon+2,nexpon+1);

% two romberg terms

rmat(2,2:3) = srinv.^rombexpon;

rmat(3,2:3) = srinv.^(2*rombexpon);

rmat(4,2:3) = srinv.^(3*rombexpon);


% qr factorization used for the extrapolation as well

% as the uncertainty estimates

[qromb,rromb] = qr(rmat,0);


% the noise amplification is further amplified by the Romberg step.

% amp = cond(rromb);


% this does the extrapolation to a zero step size.

ne = length(der_init);

rhs = vec2mat(der_init,nexpon+2,ne - (nexpon+2));

rombcoefs = rromb\(qromb'*rhs);

der_romb = rombcoefs(1,:)';


% uncertainty estimate of derivative prediction

s = sqrt(sum((rhs - rmat*rombcoefs).^2,1));

rinv = rromb\eye(nexpon+1);

cov1 = sum(rinv.^2,2); % 1 spare dof

errest = s'*12.7062047361747*sqrt(cov1(1));


end % rombextrap



% ============================================

% subfunction - vec2mat

% ============================================

function mat = vec2mat(vec,n,m)

% forms the matrix M, such that M(i,j) = vec(i+j-1)

[i,j] = ndgrid(1:n,0:m-1);

ind = i+j;

mat = vec(ind);

if n==1

  mat = mat';

end


end % vec2mat


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