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  2. Second-order cone programming - Wikipedia

    en.wikipedia.org/wiki/Second-order_cone_programming

    The "second-order cone" in SOCP arises from the constraints, which are equivalent to requiring the affine function (+, +) to lie in the second-order cone in +. [ 1 ] SOCPs can be solved by interior point methods [ 2 ] and in general, can be solved more efficiently than semidefinite programming (SDP) problems. [ 3 ]

  3. Conic optimization - Wikipedia

    en.wikipedia.org/wiki/Conic_optimization

    Examples of include the positive orthant + = {:}, positive semidefinite matrices +, and the second-order cone {(,): ‖ ‖}. Often f {\displaystyle f\ } is a linear function, in which case the conic optimization problem reduces to a linear program , a semidefinite program , and a second order cone program , respectively.

  4. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    In LP, the objective and constraint functions are all linear. Quadratic programming are the next-simplest. In QP, the constraints are all linear, but the objective may be a convex quadratic function. Second order cone programming are more general. Semidefinite programming are more general. Conic optimization are even more general - see figure ...

  5. Quadratically constrained quadratic program - Wikipedia

    en.wikipedia.org/wiki/Quadratically_constrained...

    There are two main relaxations of QCQP: using semidefinite programming (SDP), and using the reformulation-linearization technique (RLT). For some classes of QCQP problems (precisely, QCQPs with zero diagonal elements in the data matrices), second-order cone programming (SOCP) and linear programming (LP) relaxations providing the same objective value as the SDP relaxation are available.

  6. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    The idea is to substitute the constraint into the objective function to create a composite function that incorporates the effect of the constraint. For example, assume the objective is to maximize f ( x , y ) = x ⋅ y {\displaystyle f(x,y)=x\cdot y} subject to x + y = 10 {\displaystyle x+y=10} .

  7. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    When the objective function is twice differentiable, these cases can be distinguished by checking the second derivative or the matrix of second derivatives (called the Hessian matrix) in unconstrained problems, or the matrix of second derivatives of the objective function and the constraints called the bordered Hessian in

  8. You're hired: Trump names 'The Apprentice' producer Mark ...

    www.aol.com/news/youre-hired-trump-names...

    Reality TV producer Mark Burnett ('The Apprentice," "The Voice," and "Survivor") was named the UK special envoy by President-elect Donald Trump.

  9. Ordered vector space - Wikipedia

    en.wikipedia.org/wiki/Ordered_vector_space

    A subset of a vector space is called a cone if for all real >,.A cone is called pointed if it contains the origin. A cone is convex if and only if +. The intersection of any non-empty family of cones (resp. convex cones) is again a cone (resp. convex cone); the same is true of the union of an increasing (under set inclusion) family of cones (resp. convex cones).