a Rank Minimization Heuristic with Application to Minimum Order System Approximation

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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] \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] \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]X\in \real^{m\times n}[/math], which is the nuclear norm of [math]X[/math] denoted by [math]\|X\|_*[/math].

[math] \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]\|X\|_*=\sum_{i=1}^{\min\{m,n\} }\sigma_i(X)[/math] where [math] \sigma_i(X) = \sqrt{\lambda_i (X^TX)}[/math].

The nuclear norm is dual of the spectrial norm [math]\|X\|_* =\sup \{ \mbox{Tr } Y^T X | \|Y\| \leq 1 \}[/math]. So the relaxed version of the rank minimization problem is a convex optimization problem.

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

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

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

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


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