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  2. Lattice problem - Wikipedia

    en.wikipedia.org/wiki/Lattice_problem

    This is an illustration of the shortest vector problem (basis vectors in blue, shortest vector in red). In the SVP, a basis of a vector space V and a norm N (often L 2) are given for a lattice L and one must find the shortest non-zero vector in V, as measured by N, in L.

  3. Optimal estimation - Wikipedia

    en.wikipedia.org/wiki/Optimal_estimation

    In the case of a retrieval of profile information, it typical indicates the altitude resolution for a given altitude. For instance if the resolution vectors for all the altitudes contain non-zero elements (to a numerical tolerance) in their four nearest neighbours, then the altitude resolution is only one fourth that of the actual grid size.

  4. Linear complementarity problem - Wikipedia

    en.wikipedia.org/wiki/Linear_complementarity_problem

    The minimum of f is 0 at z if and only if z solves the linear complementarity problem. If M is positive definite, any algorithm for solving (strictly) convex QPs can solve the LCP. Specially designed basis-exchange pivoting algorithms, such as Lemke's algorithm and a variant of the simplex algorithm of Dantzig have been used for decades ...

  5. LOBPCG - Wikipedia

    en.wikipedia.org/wiki/LOBPCG

    Kantorovich in 1948 proposed calculating the smallest eigenvalue of a symmetric matrix by steepest descent using a direction = of a scaled gradient of a Rayleigh quotient = (,) / (,) in a scalar product (,) = ′, with the step size computed by minimizing the Rayleigh quotient in the linear span of the vectors and , i.e. in a locally optimal manner.

  6. Block Wiedemann algorithm - Wikipedia

    en.wikipedia.org/wiki/Block_Wiedemann_algorithm

    D. Coppersmith, Solving homogeneous linear equations over GF(2) via block Wiedemann algorithm, Math. Comp. 62 (1994), 333-350. Villard's 1997 research report ' A study of Coppersmith's block Wiedemann algorithm using matrix polynomials ' (the cover material is in French but the content in English) is a reasonable description.

  7. Vector projection - Wikipedia

    en.wikipedia.org/wiki/Vector_projection

    The vector projection (also known as the vector component or vector resolution) of a vector a on (or onto) a nonzero vector b is the orthogonal projection of a onto a straight line parallel to b. The projection of a onto b is often written as proj b ⁡ a {\displaystyle \operatorname {proj} _{\mathbf {b} }\mathbf {a} } or a ∥ b .

  8. Rayleigh–Ritz method - Wikipedia

    en.wikipedia.org/wiki/Rayleigh–Ritz_method

    Having found one set (left of right) of approximate singular vectors and singular values by applying naively the Rayleigh–Ritz method to the Hermitian normal matrix or , whichever one is smaller size, one could determine the other set of left of right singular vectors simply by dividing by the singular values, i.e., = / and = /. However, the ...

  9. Resolution of singularities - Wikipedia

    en.wikipedia.org/wiki/Resolution_of_singularities

    In algebraic geometry, the problem of resolution of singularities asks whether every algebraic variety V has a resolution, which is a non-singular variety W with a proper birational map W→V. For varieties over fields of characteristic 0 , this was proved by Heisuke Hironaka in 1964; [ 1 ] while for varieties of dimension at least 4 over ...