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  2. Tridiagonal matrix algorithm - Wikipedia

    en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm

    Indeed, multiplying each equation of the second auxiliary system by , adding with the corresponding equation of the first auxiliary system and using the representation = +, we immediately see that equations number 2 through n of the original system are satisfied; it only remains to satisfy equation number 1.

  3. Eigen (C++ library) - Wikipedia

    en.wikipedia.org/wiki/Eigen_(C++_library)

    Eigen is a high-level C++ library of template headers for linear algebra, matrix and vector operations, geometrical transformations, numerical solvers and related algorithms. . Eigen is open-source software licensed under the Mozilla Public License 2.0 since version 3.1

  4. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    The conjugate gradient method with a trivial modification is extendable to solving, given complex-valued matrix A and vector b, the system of linear equations = for the complex-valued vector x, where A is Hermitian (i.e., A' = A) and positive-definite matrix, and the symbol ' denotes the conjugate transpose.

  5. Alternating-direction implicit method - Wikipedia

    en.wikipedia.org/wiki/Alternating-direction...

    In numerical linear algebra, the alternating-direction implicit (ADI) method is an iterative method used to solve Sylvester matrix equations.It is a popular method for solving the large matrix equations that arise in systems theory and control, [1] and can be formulated to construct solutions in a memory-efficient, factored form.

  6. Levenberg–Marquardt algorithm - Wikipedia

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

    This equation is an example of very sensitive initial conditions for the Levenberg–Marquardt algorithm. One reason for this sensitivity is the existence of multiple minima — the function cos ⁡ ( β x ) {\displaystyle \cos \left(\beta x\right)} has minima at parameter value β ^ {\displaystyle {\hat {\beta }}} and β ^ + 2 n π ...

  7. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    HiGHS has an interior point method implementation for solving LP problems, based on techniques described by Schork and Gondzio (2020). [10] It is notable for solving the Newton system iteratively by a preconditioned conjugate gradient method, rather than directly, via an LDL* decomposition. The interior point solver's performance relative to ...

  8. Computer algebra - Wikipedia

    en.wikipedia.org/wiki/Computer_algebra

    Symbolic integration of the algebraic function f(x) = ⁠ x / √ x 4 + 10x 2 − 96x − 71 ⁠ using the computer algebra system Axiom. In mathematics and computer science, [1] computer algebra, also called symbolic computation or algebraic computation, is a scientific area that refers to the study and development of algorithms and software for manipulating mathematical expressions and other ...

  9. Newton's method - Wikipedia

    en.wikipedia.org/wiki/Newton's_method

    One may also use Newton's method to solve systems of k equations, which amounts to finding the (simultaneous) zeroes of k continuously differentiable functions :. This is equivalent to finding the zeroes of a single vector-valued function F : R k → R k . {\displaystyle F:\mathbb {R} ^{k}\to \mathbb {R} ^{k}.}