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2. Non-linear least squares - Wikipedia

   en.wikipedia.org/wiki/Non-linear_least_squares

   Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.

3. Levenberg–Marquardt algorithm - Wikipedia

   en.wikipedia.org/wiki/Levenberg–Marquardt...

   In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve fitting .

4. Least squares - Wikipedia

   en.wikipedia.org/wiki/Least_squares

   The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

5. Gauss–Newton algorithm - Wikipedia

   en.wikipedia.org/wiki/Gauss–Newton_algorithm

   Non-linear least squares problems arise, for instance, in non-linear regression, where parameters in a model are sought such that the model is in good agreement with available observations. The method is named after the mathematicians Carl Friedrich Gauss and Isaac Newton , and first appeared in Gauss's 1809 work Theoria motus corporum ...

6. Powell's dog leg method - Wikipedia

   en.wikipedia.org/wiki/Powell's_dog_leg_method

   Powell's dog leg method, also called Powell's hybrid method, is an iterative optimisation algorithm for the solution of non-linear least squares problems, introduced in 1970 by Michael J. D. Powell. [1] Similarly to the Levenberg–Marquardt algorithm, it combines the Gauss–Newton algorithm with gradient descent, but it uses an explicit trust ...

7. Nonlinear programming - Wikipedia

   en.wikipedia.org/wiki/Nonlinear_programming

   an infeasible problem is one for which no set of values for the choice variables satisfies all the constraints. That is, the constraints are mutually contradictory, and no solution exists; the feasible set is the empty set. unbounded problem is a feasible problem for which the objective function can be made to be better than any given finite ...

8. Nonlinear regression - Wikipedia

   en.wikipedia.org/wiki/Nonlinear_regression

   In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations (iterations).

9. List of numerical analysis topics - Wikipedia

   en.wikipedia.org/wiki/List_of_numerical_analysis...

   Nonlinear complementarity problem (NCP) — find x such that x ≥ 0, f(x) ≥ 0 and x T f(x) = 0; Least squares — the objective function is a sum of squares Non-linear least squares; Gauss–Newton algorithm. BHHH algorithm — variant of Gauss–Newton in econometrics; Generalized Gauss–Newton method — for constrained nonlinear least ...