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Second-order cone programming : ウィキペディア英語版
Second-order cone programming

A second-order cone program (SOCP) is a convex optimization problem of the form
:minimize \ f^T x \
:subject to
::\lVert A_i x + b_i \rVert_2 \leq c_i^T x + d_i,\quad i = 1,\dots,m
::Fx = g \
where the problem parameters are f \in \mathbb^n, \ A_i \in \mathbb^\times n}, \ b_i \in \mathbb^, \ c_i \in \mathbb^n, \ d_i \in \mathbb, \ F \in \mathbb^, and g \in \mathbb^p. Here x\in\mathbb^n is the optimization variable.
〔}〕
When A_i = 0 for i = 1,\dots,m, the SOCP reduces to a linear program. When c_i = 0 for i = 1,\dots,m, the SOCP is equivalent to a convex quadratically constrained linear program. Quadratically constrained quadratic programs can also be formulated as SOCPs by reformulating the objective function as a constraint. Semidefinite programming subsumes SOCPs as the SOCP constraints can be written as linear matrix inequalities (LMI) and can be reformulated as an instance of semi definite program. SOCPs can be solved with great efficiency by interior point methods.
==Example: Quadratic constraint==
Consider a quadratic constraint of the form
: x^T A^T A x + b^T x + c \leq 0.
This is equivalent to the SOC constraint
: \left\|
\begin
(1 + b^T x +c)/2\\
Ax
\end \right\|_2
\leq (1 - b^T x -c)/2.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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