Jacobian Multiply Function with Linear Least Squares - MATLAB & Simulink (original) (raw)

Using a Jacobian multiply function, you can solve a least-squares problem of the form

minx12‖C⋅x-d‖22

such that lb ≤ x ≤ ub, for problems where C is very large, perhaps too large to be stored. For this technique, use the 'trust-region-reflective' algorithm.

For example, consider a problem where C is a 2n-by-n matrix based on a circulant matrix. The rows of C are shifts of a row vector v. This example has the row vector v with elements of the form (–1)k+1/k:

v=[1,-1/2,1/3,-1/4,…,-1/n],

where the elements are cyclically shifted.

C=[1-1/21/3...-1/n-1/n1-1/2...1/(n-1)1/(n-1)-1/n1...-1/(n-2)⋮⋮⋮⋱⋮-1/21/3-1/4...11-1/21/3...-1/n-1/n1-1/2...1/(n-1)1/(n-1)-1/n1...-1/(n-2)⋮⋮⋮⋱⋮-1/21/3-1/4...1].

This least-squares example considers the problem where

d=[n-1,n-2,…,-n],

and the constraints are -5≤xi≤5 for i=1,…,n.

For large enough n, the dense matrix C does not fit into computer memory (n=10,000 is too large on one tested system).

A Jacobian multiply function has the following syntax.

w = jmfcn(Jinfo,Y,flag)

Jinfo is a matrix the same size as C, used as a preconditioner. If C is too large to fit into memory, Jinfo should be sparse. Y is a vector or matrix sized so that C*Y or C'*Y works as matrix multiplication. flag tells jmfcn which product to form:

Because C is such a simply structured matrix, you can easily write a Jacobian multiply function in terms of the vector v, without forming C. Each row of C*Y is the product of a circularly shifted version of v times Y. Use circshift to circularly shift v.

To compute C*Y, compute v*Y to find the first row, then shift v and compute the second row, and so on.

To compute C'*Y, perform the same computation, but use a shifted version of temp, the vector formed from the first row of C':

temp = [fliplr(v),fliplr(v)];

temp = [circshift(temp,1,2),circshift(temp,1,2)]; % Now temp = C'(1,:)

To compute C'*C*Y, simply compute C*Y using shifts of v, and then compute C' times the result using shifts of fliplr(v).

The helper function lsqcirculant3 is a Jacobian multiply function that implements this procedure; it appears at the end of this example.

The dolsqJac3 helper function at the end of this example sets up the vector v and calls the solver lsqlin using the lsqcirculant3 Jacobian multiply function.

When n = 3000, C is an 18,000,000-element dense matrix. Determine the results of the dolsqJac3 function for n = 3000 at selected values of x, and display the output structure.

[x,resnorm,residual,exitflag,output] = dolsqJac3(3000);

Local minimum possible.

lsqlin stopped because the relative change in function value is less than the function tolerance.

     iterations: 16
      algorithm: 'trust-region-reflective'
  firstorderopt: 5.9351e-05
   cgiterations: 36
constrviolation: []
   linearsolver: []
        message: 'Local minimum possible.↵↵lsqlin stopped because the relative change in function value is less than the function tolerance.'

Helper Functions

This code creates the lsqcirculant3 helper function.

function w = lsqcirculant3(Jinfo,Y,flag,v) % This function computes the Jacobian multiply function % for a 2n-by-n circulant matrix example.

if flag > 0 w = Jpositive(Y); elseif flag < 0 w = Jnegative(Y); else w = Jnegative(Jpositive(Y)); end

function a = Jpositive(q)
    % Calculate C*q
    temp = v;

    a = zeros(size(q)); % Allocating the matrix a
    a = [a;a]; % The result is twice as tall as the input.

    for r = 1:size(a,1)
        a(r,:) = temp*q; % Compute the rth row
        temp = circshift(temp,1,2); % Shift the circulant
    end
end

function a = Jnegative(q)
    % Calculate C'*q
    temp = fliplr(v);
    temp = circshift(temp,1,2); % Shift the circulant for C'

    len = size(q,1)/2; % The returned vector is half as long
    % as the input vector.
    a = zeros(len,size(q,2)); % Allocating the matrix a

    for r = 1:len
        a(r,:) = [temp,temp]*q; % Compute the rth row
        temp = circshift(temp,1,2); % Shift the circulant
    end
end

end

This code creates the dolsqJac3 helper function.

function [x,resnorm,residual,exitflag,output] = dolsqJac3(n) % r = 1:n-1; % Index for making vectors

v(n) = (-1)^(n+1)/n; % Allocating the vector v v(r) =( -1).^(r+1)./r;

% Now C should be a 2n-by-n circulant matrix based on v, % but it might be too large to fit into memory.

r = 1:2*n; d(r) = n-r;

Jinfo = [speye(n);speye(n)]; % Sparse matrix for preconditioning % This matrix is a required input for the solver; % preconditioning is not used in this example.

% Pass the vector v so that it does not need to be % computed in the Jacobian multiply function. options = optimoptions('lsqlin','Algorithm','trust-region-reflective',... 'JacobianMultiplyFcn',@(Jinfo,Y,flag)lsqcirculant3(Jinfo,Y,flag,v));

lb = -5ones(1,n); ub = 5ones(1,n);

[x,resnorm,residual,exitflag,output] = ... lsqlin(Jinfo,d,[],[],[],[],lb,ub,[],options); end

See Also

circshift | fliplr

Topics