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Fast (1+ε)-approximation of the Löwner extremal matrices of high-dimensional symmetric matrices

Matrix data sets are common nowadays like in biomedical imaging where the Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) modality produces data sets of 3D symmetric positive definite matrices anchored at voxel positions capturing the anisotropic diffusion properties of water molecules in biological tissues. The space of symmetric matrices can be partially ordered using the Löwner ordering, and computing extremal matrices dominating a given set of matrices is a basic primitive used in matrix-valued signal processing. In this letter, we design a fast and easy-to-implement iterative algorithm to approximate arbitrarily finely these extremal matrices. Finally, we discuss on extensions to matrix clustering.


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1 Introduction: Löwner extremal matrices and their applications

Let denote the space of square matrices with real-valued coefficients, and

the matrix vector space

333Although addition preserves the symmetric property, beware that the product of two symmetric matrices may be not symmetric. of symmetric matrices. A matrix is said Symmetric Positive Definite [1] (SPD, denoted by ) iff. and only Symmetric Positive Semi-Definite444Those definitions extend to Hermitian matrices . (SPSD, denoted by ) when we relax the strict inequality (). Let denote the space of positive semi-definite matrices, and denote the space of positive definite matrices. A matrix is defined by real coefficients, and so is a SPD or a SPSD matrix. Although is a vector space, the SPSD matrix space does not have the vector space structure but is rather an abstract pointed convex cone with apex

the zero matrix

since . Symmetric matrices can be partially ordered using the Löwner ordering:555Also often written Loewner in the literature, e.g., see [2]. and . When , matrix is said to dominate matrix , or equivalently that matrix is dominated by matrix . Note that the difference of two SPSD matrices may not be a SPSD matrix.666For example, consider and then and . A non-SPSD symmetric matrix can be dominated by a SPSD matrix when .777 For example, is dominated by

(by taking the absolute values of the eigenvalues of


The supremum operator is defined on symmetric matrices (not necessarily SPSDs) as follows:

Problem 1 (Löwner maximal matrices)

where .

This matrix is indeed the “smallest”, meaning the tightest upper bound, since by definition there does not exist another symmetric matrix dominating all the ’s and dominated by . Trivially, when there exists a matrix that dominates all others of a set , then the supremum of that set is matrix . Similarly, we define the minimal/infimum matrix as the tightest lower bound. Since matrix inversion reverses the Löwner ordering (), we link those extremal supremum/infimum matrices when considering sets of invertible symmetric matrices as follows: . Extremal matrices are rotational invariant , where

is any orthogonal matrix (

). This property is important in DT-MRI processing that should be invariant to the chosen reference frame.

Computing Löwner extremal matrices are useful in many applications: For example, in matrix-valued imaging [3, 4] (morphological operations, filtering, denoising or image pyramid representations), in formal software verification [5], in statistical inference with domain constraints [6, 7]

, in structure tensor of computer vision 

[8] (Förstner-like operators), etc.

This letter is organized as follows: Section 2 explains how to transform the extremal matrix problem into an equivalent geometric minimum enclosing ball of balls. Section 3 presents a fast iterative approximation algorithm that scales well in high-dimensions. Section 4 concludes by hinting at further perspectives.

2 Equivalent geometric covering problems

We build on top of [9] to prove that solving the -dimensional Löwner maximal matrix amounts to either find (1) the minimal covering Löwner matrix cone (wrt. set containment ) of a corresponding sets of -dimensional cones (with ), or (2) the minimal enclosing ball of a set of corresponding -dimensional “matrix balls” that we cast into a geometric vector ball covering problem for amenable computations.

2.1 Minimal matrix/vector cone covering problems

Let denote the Löwner ordering cone , and the reverted and translated dominance cone (termed the penumbra cone in [9]) with apex embedded in the space of symmetric matrices that represents all the symmetric matrices dominated by : , where denotes the Minkowski set subtraction operator: (hence, ). A matrix dominates iff. . In plain words, dominates a set of matrices iff. its associated dominance cone covers all the dominance cones for . The dominance cones are “abstract” cones defined in the symmetric matrix space that can be “visualized” as equivalent vector cones in dimension using half-vectorization: For a symmetric matrix , we stack the elements of the lower-triangular matrix part of (with ): . Note that this is not the unique way to half-vectorize symmetric matrices but it is enough for geometric containment purposes. Later, we shall enforce that the -norm of vectors matches the Fröbenius matrix norm .

Let denotes the vectorized matrix Löwner ordering cone: , and denote the vector dominance cone: . Next, we further transform this minimum -dimensional matrix/vector cone covering problems as equivalent Minimum Enclosing Ball (MEB) problems of -dimensional matrix/vector balls.

2.2 Minimum enclosing ball of ball problems

A basis of a convex cone anchored at the origin is a convex subset so that there exists a unique decomposition: with and . For example, is a basis of the Löwner cone . Informally speaking, a basis of a cone can be interpreted as a compact cross-section of the cone. The Löwner cone is a smooth convex cone with its interior denoting the space of positive definite matrices (full rank matrices), and its border the rank-deficient symmetric positive semi-definite matrices (with apex the zero matrix of rank ). A point is an extreme element of a convex set iff. remains convex. It follows from Minkowski theorem that every compact convex set in a finite-dimensional vector space can be reconstructed as convex combinations of its extreme points : That is, the compact convex set is the closed convex hull of its extreme points.

Figure 1: The dominance cone associated with matrix has apex and base , a ball centered at matrix of radius . The cone has an equivalent representation provided that .

A face of a closed cone is a subcone such that . The -dimensional faces are the extremal rays of the cone. The basis of the Löwner ordering cone is [10] . Other rank-deficient or full rank matrices can be constructed by convex combinations of these rank- matrices, the extremal rays.

For any square matrix , the trace operator is defined by , the sum of the diagonal elements of the matrix. The trace also amounts to the sum of the eigenvalues of matrix : . The basis of a dominance cone is . Note that all the basis of the dominance cones lie in the subspace of symmetric matrices with zero trace. Let denote the matrix inner product and the matrix Fröbenius norm. Two matrices and are orthogonal (or perpendicular) iff.

. It can be checked that the identity matrix

is perpendicular to any zero-trace matrix since . The center of the ball basis of the dominance cone is obtained as the orthogonal projection of onto the zero-trace subspace : . The dominance cone basis is a matrix ball since for any rank- matrix with (an extreme point), we have the radius:


that is non-negative since we assumed that . Reciprocally, to a basis ball , we can associate the apex of its corresponding dominance cone : . Figure 1 illustrates the notations and the representation of a cone by its corresponding basis and apex. Thus we associate to each dominance cone its corresponding ball basis on the subspace of zero trace matrices: , . We have the following containment relationships: and

Finally, we transform this minimum enclosing matrix ball problem into a minimum enclosing vector ball problem using a half-vectorization that preserves the notion of distances, i.e., using an isomorphism between the space of symmetric matrices and the space of half-vectorized matrices. The -norm of the vectorized matrix should match the matrix Fröbenius norm: . Since , it follows that . We can convert back a vector into a corresponding symmetric matrix.

Since we have considered all dominance cones with basis rooted on in order to compute the ball basis as orthogonal projections, we need to pre-process the symmetric matrices to ensure that property as follows: Let denote the minimal trace of the input set of symmetric matrices , and define for where denotes the identity matrix. Recall that . By construction, the transformed input set satisfies . Furthermore, observe that iff. where , so that .

As a side note, let us point out that the reverse basis-sphere-to-cone mapping has been used to compute the convex hull of -dimensional spheres (convex homothets) from the convex hull of -dimensional equivalent points [11, 12].

Finally, let us notice that there are severals ways to majorize/minorize matrices: For example, once can seek extremal matrices that are invariant up to an invertible transformation [5], a stronger requirement than the invariance by orthogonal transformation. In the latter case, it amounts to geometrically compute the Minimum Volume Enclosing Ellipsoid of Ellipsoids (MVEEE) [5, 13].

2.3 Defining -approximations of

First, let us summarize the algorithm for computing the Löwner maximal matrix of a set of symmetric matrices as follows:

  1. Normalize matrices so that they have all non-negative traces:

  2. Compute the vector ball representations of the dominance cones:



  3. Compute the small(est) enclosing ball of basis balls (either exactly or an approximation):

  4. Convert back the small(est) enclosing ball to the dominance cone, and recover its apex :

  5. Adjust back the matrix trace:

Computing exactly the extremal Löwner matrices suffer from the curse of dimensionality of computing MEBs [14]. In [9], Burgeth et al. proceed by discretizing the basis spheres by sampling888In 2D, we sample for . In 3D, we use spherical coordinates for and . the extreme x points for . This yields an approximation term, requires more computation, and even worse the method does not scale [15] in high-dimensions. Thus in order to handle high-dimensional matrices met in software formal verification [5] or in computer vision (structure tensor [8]), we consider -approximation of the extremal Löwner matrices. The notion of tightness of approximation of (the epsilon) is imported straightforwardly from the definition of the tightness of the geometric covering problems. A -approximation of is a matrix such that: . It follows from Eq. 2 that a -approximation satisfies .

We present a fast guaranteed approximation algorithm for approximating the minimum enclosing ball of a set of balls (or more generally, for sets of compact geometric objects).

3 Approximating the minimum enclosing ball of objects and balls

Figure 2: Approximating the minimum enclosing ball of balls iteratively: Snapshots at iterations 1, 2, 3,1008, 2008 and 3008 (best viewed in color).

We extend the incremental algorithm of Bădoiu and Clarkson [16] (BC) designed for finite point sets to ball sets or compact object sets that work in large dimensions. Let denote a set of balls. For an object and a query point , denote by the farthest distance from to : , and let denote the farthest point of from . The generalized BC  [16] algorithm for approximating the circumcenter of the minimum volume enclosing ball of objects (MVBO) is summarized as follows:

  • Let and .

  • Repeat times:

    • Find the farthest object to current center:

    • Update the circumcenter:

    • .

When considering balls as objects, the farthest distance of a point to a ball is , and the circumcenter updating rule is: . See Figure 2 and online video999 for an illustration. (MVBO can also be used to approximate the MEB of ellipsoids.) It is proved in [17] that at iteration , we have where is the unique smallest enclosing ball. Hence the radius of the ball centered at is bounded by . To get a -approximation, we need iterations.s It follows that a -approximation of the smallest enclosing ball of -dimensional balls can be computed in -time [17], and since we get:

Theorem 1

The Löwner maximal matrix of a set of -dimensional symmetric matrices can be approximated by a matrix such that in -time.

Interestingly, this shows that the approximation of Löwner supremum matrices admits core-sets [17], the subset of farthest balls chosen during the iterations, so that with . See [18] for other MEB approximation algorithms.

To a symmetric matrix , we associate a quadratic form that is a strictly convex function when is PSD. Therefore, we may visualize the SPSD matrices in 2D/3D as ellipsoids (potentially degenerated flat ellipsoids for rank-deficient matrices). More precisely, we associate to each positive definite matrix , a geometric ellipsoid defined by , where is a prescribed constant (usually set to , Figure 3). From the SVD decomposition of , we recover the rotation matrix, and the semi-radii of the ellipsoid are the square root eigenvalues . It follows that . To handle degenerate flat ellipsoids that are not fully dimensional (rank-deficient matrix ), we define

. Note that those ellipsoids are all centered at the origin, and may also conceptually be thought as centered Gaussian distributions (or covariance matrices denoting the concentration ellipsoids of estimators 

[2] in statistics). We can also visualize the Löwner ordering cone and dominance cones for matrices embedded in the vectorized 3D space of symmetric matrices (Figure 3), and the corresponding half-vectorized ball basis (Figure 3).

(a) (b) (c)
Figure 3: Equivalent visualizations: (a) PSD matrices visualized as ellipsoids, with (b) corresponding 3D vector Löwner cones, and (c) corresponding cone vector ball basis.

4 Concluding remarks

Our novel extremal matrix approximation method allows one to leverage further related results related to core-sets [16] for dealing with high-dimensional extremal matrices. For example, we may consider clustering PSD matrices with respect to Löwner order and use the -center clustering technique with guaranteed approximation [19, 20]. A Java™ code of our method is available for reproducible research.


This work was carried out during the Matrix Information Geometry (MIG) workshop [21], organized at École Polytechnique, France in February 2011 ( Frank Nielsen dedicates this work to the memory of his late father Gudmund Liebach Nielsen who passed away during the last day of the workshop.


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