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  2. Symmetric algebra - Wikipedia

    en.wikipedia.org/wiki/Symmetric_algebra

    The symmetric algebra S(V) can be built as the quotient of the tensor algebra T(V) by the two-sided ideal generated by the elements of the form x ⊗ y − y ⊗ x. All these definitions and properties extend naturally to the case where V is a module (not necessarily a free one) over a commutative ring .

  3. Multigrid method - Wikipedia

    en.wikipedia.org/wiki/Multigrid_method

    Its main advantage versus a purely multigrid solver is particularly clear for nonlinear problems, e.g., eigenvalue problems. If the matrix of the original equation or an eigenvalue problem is symmetric positive definite (SPD), the preconditioner is commonly constructed to be SPD as well, so that the standard conjugate gradient (CG) iterative ...

  4. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    Top: The action of M, indicated by its effect on the unit disc D and the two canonical unit vectors e 1 and e 2. Left: The action of V ⁎, a rotation, on D, e 1, and e 2. Bottom: The action of Σ, a scaling by the singular values σ 1 horizontally and σ 2 vertically.

  5. LAPACK - Wikipedia

    en.wikipedia.org/wiki/LAPACK

    LAPACK ("Linear Algebra Package") is a standard software library for numerical linear algebra.It provides routines for solving systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition.

  6. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    A generalized eigenvalue problem (second sense) is the problem of finding a (nonzero) vector v that obeys = where A and B are matrices. If v obeys this equation, with some λ , then we call v the generalized eigenvector of A and B (in the second sense), and λ is called the generalized eigenvalue of A and B (in the second sense) which ...

  7. Linear complementarity problem - Wikipedia

    en.wikipedia.org/wiki/Linear_complementarity_problem

    with v the Lagrange multipliers on the non-negativity constraints, λ the multipliers on the inequality constraints, and s the slack variables for the inequality constraints. The fourth condition derives from the complementarity of each group of variables (x, s) with its set of KKT vectors (optimal Lagrange multipliers) being (v, λ). In that case,

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