a Rank Minimization Heuristic with Application to Minimum Order System Approximation

From statwiki
Jump to navigation Jump to search

Rank Minimization Problem (RMP) has application in a variety of areas such as control, system identification, statistics and signal processing. Except in some special cases RMP is known to be computationaly hard. [math]\displaystyle{ \begin{array}{ l l } \mbox{minimize} & \mbox{Rank } X \\ \mbox{subject to: } & X \in C \end{array} }[/math]

If the matrix is symmetric and positive semidifinite, trace minimization is a very effective heuristic for rank minimization problem. The trace minimization results in a semidefinite problem which can be easily solved. [math]\displaystyle{ \begin{array}{ l l } \mbox{minimize} & \mbox{Tr } X \\ \mbox{subject to: } & X \in C \end{array} }[/math]

This paper focuses on the following problems:

  1. Describing a generalization of the trace heuristic for genaral non-square matrices.
  2. Showing that the new heuristic can be reduced to an SDP, and hence effictively solved.
  3. Applying the mothod on the minimum order system approximation.

A Generalization Of The Trace Heuristic

This heurisitic minimizes the sum of the singular values of the matrix [math]\displaystyle{ X\in \real^{m\times n} }[/math], which is the nuclear norm of [math]\displaystyle{ X }[/math] denoted by [math]\displaystyle{ |X|_* }[/math].

[math]\displaystyle{ \begin{array}{ l l } \mbox{minimize} & |X|_* \\ \mbox{subject to: } & X \in C \end{array} }[/math]

According to the definition of the nuclear norm we have [math]\displaystyle{ |X|_*=\sum_{i=1}^{\min\{m,n\} }\sigma_i(X) }[/math] where [math]\displaystyle{ \sigma_i(X) = \sqrt{\lambda_i (X^TX)} }[/math].

When the matrix variable [math]\displaystyle{ X }[/math] is symmetric and positive semidefinite, then its singular values are the same as its eigenvalues, and therefore the nuclear norm reduces to [math]\displaystyle{ \mbox{Tr } X }[/math], and that means the heuristic reduces to the trace minimization heuristic.

Nuclear Norm Minimization vs. Rank Minimization

convex envelope of a function, borrowed from <ref>Rank Minimization and Applications in System Theory, M. Fazel, H. Hindi, and S. Body</ref>

Definition: Let [math]\displaystyle{ f:C \rightarrow\real }[/math] where [math]\displaystyle{ C\subseteq \real^n }[/math]. The convex envelope of [math]\displaystyle{ f }[/math] (on [math]\displaystyle{ C }[/math]) is defined as the largest convex function [math]\displaystyle{ g }[/math] such that [math]\displaystyle{ g(x)\leq f(x) }[/math] for all [math]\displaystyle{ x\in X }[/math].

Theorem 1 The convex envelope of the function [math]\displaystyle{ \phi(X)=\mbox{Rank }(X) }[/math], on [math]\displaystyle{ C=\{X\in \real^{m\times n} | |X|\leq 1\} }[/math] is [math]\displaystyle{ \phi_{\mbox{env}}(X) = |X|_* }[/math].


Suppose [math]\displaystyle{ X\in C }[/math] is bounded by [math]\displaystyle{ M }[/math] that is [math]\displaystyle{ |X|\leq M }[/math], then the convex envelope of [math]\displaystyle{ Rank X }[/math] on [math]\displaystyle{ \{X | |X|\leq M }[/math] is given by [math]\displaystyle{ \frac{1}{M}|X|_* }[/math].


[math]\displaystyle{ \mbox{Rank } X \geq \frac{1}{M} |X|_* }[/math] That means if [math]\displaystyle{ p_{\mbox{rank}} }[/math] and [math]\displaystyle{ p_{*} }[/math] are the optimal values of the rank minimization problem and dual spectrial norm minimization problem then we have [math]\displaystyle{ p_{\mbox{rank}}\geq \frac{1}{M} p_{*} }[/math]

References

<references />