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Conic optimization

Subfield of convex 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.

01Definition

Given a real vector space X, a convex, real-valued function

f:C\to \mathbb {R}

defined on a convex cone C\subset X, and an affine subspace {\mathcal {H}} defined by a set of affine constraints h_{i}(x)=0\, a conic optimization problem is to find the point x in C\cap {\mathcal {H}} for which the number f(x) is smallest.

Examples of C include the positive orthant \mathbb {R} _{+}^{n}=\left\{x\in \mathbb {R} ^{n}:\,x\geq \mathbf {0} \right\}, positive semidefinite matrices \mathbb {S} _{+}^{n}, and the second-order cone \left\{(x,t)\in \mathbb {R} ^{n}\times \mathbb {R} :\lVert x\rVert \leq t\right\}. Often 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.

02Duality

Certain special cases of conic optimization problems have notable closed-form expressions of their dual problems.

Conic LP

The dual of the conic linear program

minimize c^{T}x\
subject to Ax=b,x\in C\

is

maximize b^{T}y\
subject to A^{T}y+s=c,s\in C^{*}\

where C^{*} denotes the dual cone of C\.

Whilst weak duality holds in conic linear programming, strong duality does not necessarily hold.

Semidefinite Program

The dual of a semidefinite program in inequality form

minimize c^{T}x\
subject to x_{1}F_{1}+\cdots +x_{n}F_{n}+G\leq 0

is given by

maximize \mathrm {tr} \ (GZ)\
subject to \mathrm {tr} \ (F_{i}Z)+c_{i}=0,\quad i=1,\dots ,n
Z\geq 0
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Sources and credits

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