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Problem 258. linear least squares fitting

Created by Tomasz


  • f: cell-array of function handles
  • x: column vector of x values
  • y: column vector of y values, same length as x


  • a: column vector of coefficients, same length as f

In a correct answer the coefficients a take values such that the function

   fit = @(x) a(1)*f{1}(x) + a(2)*f{2}(x) + a(3)*f{3}(x) +...+ a(end)*f{end}(x)

minimizes the sum of the squared deviations between fit(x) and y, i.e. sum((fit(x)-y).^2) is minimal.


  • The functions will all be vectorized, so e.g. f{1}(x) will return results for the whole vector x
  • The absolute errors of a must be smaller than 1e-6 to pass the tests

Problem Group

Solution Statistics

19 correct solutions 12 incorrect solutions
Last solution submitted on Mar 26, 2016

Problem Comments