Convex optimization nonlinear least squares

  • Is least squares a convex optimization problem?

    The Hessian of this objective for any w is given by ∇2f(w) = X X. ≥ 0, so least squares is convex (and solving ∇f(w)=0 gives global minimum)..

  • Is true or false least squares is a kind of convex optimization problem?

    Least squares is a special case of convex optimization.
    True..

  • What is nonlinear least square optimization method?

    Nonlinear Least Squares (NLS) is an optimization technique that can be used to build regression models for data sets that contain nonlinear features.
    Models for such data sets are nonlinear in their coefficients.
    PART 1: The concepts and theory underlying the NLS regression model..

  • Least squares is a special case of convex optimization.
    True.
Feb 14, 2013It depends. Once you are in the nonlinear world, things can be convex or nonconvex. You can write a generic nonlinear least-squares problem  Proof of convexity of linear least squaresHow to solve this nonlinear least square optimization?Convex vs non-convex formulations for the equivalent least squares Convexity of a non linear optimization problemMore results from math.stackexchange.com
Feb 14, 2013The last term, J(x)TJ(x) is always a positive semi-definite matrix. If the problem were a linear least-squares problem, all the individual  How to solve this nonlinear least square optimization?Proof of convexity of linear least squaresConvex vs non-convex formulations for the equivalent least squares Is constrained least squares regression convex?More results from math.stackexchange.com

Are linear least squares convex optimization?

linear least-squares are convex optimization

Are nonlinear least squares also convex optimization? Can someone please give some simple examples? I guess that would depend on which particular nonlinear function is used

Feb 14, 2013 at 14:10 The Wikipedia article gives examples with multiple minima, which obviously cannot be convex


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