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Hey all,

I'm sure someone will have a quick answer for my probably dumb question. Performing polynomial least squares regression of a set of [x,y] data. Using polyfit, polyval, and corrcoef, I solved for the standard regression.

My problem is when I need to solve for a constrained polynomial such as: f(x)=a3*x.^3+a1*x+a0

So what am I missing that leaves me with no understanding how to solve this problem.

Geoff
on 6 Mar 2012

So you mean that you want to constrain a cubic polynomial regression such that the x^2 term is zero?

For this I would use fsolve if you have the optimization toolbox.

If you don't, there is always the Nelder-Mead algorithm implemented as the function fminsearch.

You'd go something like this:

constraint = [1,0,1,1]; % Ignore possible x^2 term

polyFunc = @(p) polyval(p.*constraint,x);

objectiveFunc = @(p) (y - polyFunc(p)).^2;

p0 = [1, 0, 2, 3]; % It pays to have a realistic initial guess

p = fminsearch( objectiveFunc, p0 );

Instead of using polyval you could always just write the polynomial formula verbatim. However, this way you get a result that is consistent with other polynomial functions.

Warning: Nelder-Mead can be a hairy beast. Read the documentation, play around, and find ways to tame it.

Geoff
on 6 Mar 2012

Sorry, my mistake!

The objective function should be the SUM of squares.

objectiveFunc = @(p) sum((y - polyFunc(p)).^2);

Also, just because you constrained x^2 term to zero and supplied zero starting value for it, doesn't mean the algorithm won't output a non-zero value. So you might want to multiply the result by the constraint again:

p = p .* constraint;

Richard Willey
on 6 Mar 2012

Curve Fitting Toolbox allows you to specify constraints for individual regression coefficients.

With this said and done, could you please elaborate why you need to constrain a regression coefficient to be precisely equal to zero rather than simply leaving that term out of your model?

Mark Selby
on 6 Mar 2012

The simple first principles way is as follows...

x=(1:10)'; % invent some data for prediction

y = x.^3 + 2; % and generate some "experimental" data

% create a matrix that contains all you predictors

% note just include the terms you want

vm=[x.^3 ones(size(x))]; % the vandermode matrix

% then solve the least squares problem.

beta = vm\y %this is essentially what polyfit does.

Alternatively download "polyfitn" from the FEX.... it does what it says on the tin AND your problem with a nice interface and some help.

Sean de Wolski
on 6 Mar 2012

http://en.wikipedia.org/wiki/Vandermonde_matrix

You create a vandermonde matrix for your above expression (y = a3.*x^3+a1*x-200) such that v, contains the xpressions to solve for) and v*x=y;

V = [x(:).^3, x(:), ones(size(x(:)))]

Sean de Wolski
on 6 Mar 2012

Totally okay. And this question has yielded lots of good edumacational information, so +1.

Richard Willey
on 6 Mar 2012

The 12a release of Statistics Toolbox has some very nice new features for regression analysis. There is a new function named LinearModel for linear regression. The function implements a scripting syntax called "Wilkinson's Notation" which is designed for implementing custom linear models. (Wilkinson's Notation" is pretty standard amongst statisticians)

% Create a data set

X = linspace(1, 10, 100);

X = X';

Y = 3*X.^3 + 5*X + 7;

% Fit a linear regression to the model

myFit = LinearModel.fit(X,Y, 'y~x1:x1:x1 + x1')

plot(myFit)

methods(myFit)

The text string 'y~x1:x1:x1 + x1' translates as y is modeled as function of X^3, X, and a constant. The model excludes the term X^2 which is equivalent to setting the coefficient equal to zero.

Roland
on 3 Jul 2019

For others looking into polynomial regression with constraints:

A general framework for constraint polynomial approximation (value constraints, derivative constraints, coefficient constraints) can be found in a paper of me and my colleagues:

- O’Leary, P., Ritt, R. and Harker, M. (2019) ‘Constrained Polynomial Approximation for Inverse Problems in Engineering’, in Wahab, M. A. (ed.) Proceedings of the 1st International Conference on Numerical Modelling in Engineering. Springer Singapore, pp. 225–244. doi: 10.1007/978-981-13-2273-0_19.

Kin Sung Chan
on 30 Sep 2019

One way is to use the fittype and fix the lower and upper bound. (x,y) are some vectors that you want to fit.

The good thing about this method is you can fit with whatever equation you like.

s = fitoptions('Method','NonlinearLeastSquares',...

'Lower',[10 -10 ],...

'Upper',[10 10 ],...

'MaxIter',10000,...

'TolX',1E-8,...

'Startpoint',[10,0]);

f = fittype('a1*x+b','options',s);

ffit = fit(x, y, f)

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