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  2. Levenberg–Marquardt algorithm - Wikipedia

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

    The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters ⁠ ⁠ of the model curve (,) so that the sum of the squares of the deviations () is minimized:

  3. Newton–Krylov method - Wikipedia

    en.wikipedia.org/wiki/Newton–Krylov_method

    Newton–Krylov methods are numerical methods for solving non-linear problems using Krylov subspace linear solvers. [1] [2] Generalising the Newton method to systems of multiple variables, the iteration formula includes a Jacobian matrix. Solving this directly would involve calculation of the Jacobian's inverse, when the Jacobian matrix itself ...

  4. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    Some special cases of nonlinear programming have specialized solution methods: If the objective function is concave (maximization problem), or convex (minimization problem) and the constraint set is convex, then the program is called convex and general methods from convex optimization can be used in most cases.

  5. Explicit and implicit methods - Wikipedia

    en.wikipedia.org/wiki/Explicit_and_implicit_methods

    Explicit methods calculate the state of a system at a later time from the state of the system at the current time, while implicit methods find a solution by solving an equation involving both the current state of the system and the later one.

  6. Gauss–Newton algorithm - Wikipedia

    en.wikipedia.org/wiki/Gauss–Newton_algorithm

    Since a sum of squares must be nonnegative, the algorithm can be viewed as using Newton's method to iteratively approximate zeroes of the components of the sum, and thus minimizing the sum. In this sense, the algorithm is also an effective method for solving overdetermined systems of equations. It has the advantage that second derivatives ...

  7. Numerical continuation - Wikipedia

    en.wikipedia.org/wiki/Numerical_continuation

    Numerical continuation is a method of computing approximate solutions of a system of parameterized nonlinear equations, F ( u , λ ) = 0. {\displaystyle F(\mathbf {u} ,\lambda )=0.} [ 1 ] The parameter λ {\displaystyle \lambda } is usually a real scalar and the solution u {\displaystyle \mathbf {u} } is an n -vector .

  8. Relaxation (iterative method) - Wikipedia

    en.wikipedia.org/wiki/Relaxation_(iterative_method)

    Relaxation methods are used to solve the linear equations resulting from a discretization of the differential equation, for example by finite differences. [ 2 ] [ 3 ] [ 4 ] Iterative relaxation of solutions is commonly dubbed smoothing because with certain equations, such as Laplace's equation , it resembles repeated application of a local ...

  9. Iterative method - Wikipedia

    en.wikipedia.org/wiki/Iterative_method

    In the absence of rounding errors, direct methods would deliver an exact solution (for example, solving a linear system of equations = by Gaussian elimination). Iterative methods are often the only choice for nonlinear equations. However, iterative methods are often useful even for linear problems involving many variables (sometimes on the ...

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