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  2. Dilution of precision (navigation) - Wikipedia

    en.wikipedia.org/wiki/Dilution_of_precision...

    Thus a low DOP value represents a better positional precision due to the wider angular separation between the satellites used to calculate a unit's position. Other factors that can increase the effective DOP are obstructions such as nearby mountains or buildings. DOP can be expressed as a number of separate measurements: HDOP

  3. Nucleolus (game theory) - Wikipedia

    en.wikipedia.org/wiki/Nucleolus_(game_theory)

    In cooperative game theory, the nucleolus of a cooperative game is the solution (i.e., allocation of payments to players) that maximizes the smallest excess of a coalition (where the excess is the difference between the payment given to the coalition and the value the coalition could get by deviating).

  4. Second-order cone programming - Wikipedia

    en.wikipedia.org/wiki/Second-order_cone_programming

    We refer to second-order cone programs as deterministic second-order cone programs since data defining them are deterministic. Stochastic second-order cone programs are a class of optimization problems that are defined to handle uncertainty in data defining deterministic second-order cone programs. [10]

  5. Semidefinite programming - Wikipedia

    en.wikipedia.org/wiki/Semidefinite_programming

    Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron.

  6. Sprague–Grundy theorem - Wikipedia

    en.wikipedia.org/wiki/Sprague–Grundy_theorem

    For the purposes of the Sprague–Grundy theorem, a game is a two-player sequential game of perfect information satisfying the ending condition (all games come to an end: there are no infinite lines of play) and the normal play condition (a player who cannot move loses).

  7. Conic optimization - Wikipedia

    en.wikipedia.org/wiki/Conic_optimization

    Conic optimization is a subfield of convex optimization that studies problems consisting of minimizing a convex function over the intersection of an affine subspace and a convex cone. The class of conic optimization problems includes some of the most well known classes of convex optimization problems, namely linear and semidefinite programming.

  8. Bounding volume - Wikipedia

    en.wikipedia.org/wiki/Bounding_volume

    Thus, a k-DOP is the Boolean intersection of k bounding slabs and is a convex polytope containing the object (in 2-D a polygon; in 3-D a polyhedron). A 2-D rectangle is a special case of a 2-DOP, and a 3-D box is a special case of a 3-DOP. In general, the axes of a DOP do not have to be orthogonal, and there can be more axes than dimensions of ...

  9. Hypercone - Wikipedia

    en.wikipedia.org/wiki/Hypercone

    In geometry, a hypercone (or spherical cone) is the figure in the 4-dimensional Euclidean space represented by the equation x 2 + y 2 + z 2 − w 2 = 0. {\displaystyle x^{2}+y^{2}+z^{2}-w^{2}=0.} It is a quadric surface, and is one of the possible 3- manifolds which are 4-dimensional equivalents of the conical surface in 3 dimensions.