Log-majorizations between quasi-geometric type means for matrices
- URL: http://arxiv.org/abs/2510.04691v2
- Date: Mon, 20 Oct 2025 02:05:27 GMT
- Title: Log-majorizations between quasi-geometric type means for matrices
- Authors: Fumio Hiai,
- Abstract summary: The log-majorization $mathcalM_alpha,p(A,B)prec_logmathcalN_alpha,q(A,B)$ is examined for pairs $(mathcalM,calN)$ those $alpha$-weighted geometric type means.<n>The joint concavity/ity of the trace functions $mathrmTr,mathcalM_alpha,p$ is also discussed based on theory of quantum divergences.
- Score: 4.56877715768796
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, for $\alpha\in(0,\infty)\setminus\{1\}$, $p>0$ and positive semidefinite matrices $A$ and $B$, we consider the quasi-extension $\mathcal{M}_{\alpha,p}(A,B):=\mathcal{M}_\alpha(A^p,B^p)^{1/p}$ of several $\alpha$-weighted geometric type matrix means $\mathcal{M}_\alpha(A,B)$ such as the $\alpha$-weighted geometric mean in Kubo--Ando's sense, the R\'enyi mean, etc. The log-majorization $\mathcal{M}_{\alpha,p}(A,B)\prec_{\log}\mathcal{N}_{\alpha,q}(A,B)$ is examined for pairs $(\mathcal{M},\mathcal{N})$ of those $\alpha$-weighted geometric type means. The joint concavity/convexity of the trace functions $\mathrm{Tr}\,\mathcal{M}_{\alpha,p}$ is also discussed based on theory of quantum divergences.
Related papers
- Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling [5.142160533428576]
We compute the eigenvalue distribution of the neural tangent kernel of a two-layer neural network under a specific scaling of dimension.<n>We describe the distribution as the free multiplicative convolution of the Marchenko--Pastur distribution with a deterministic distribution depending on $sigma$ and $D$.
arXiv Detail & Related papers (2025-08-27T16:41:01Z) - Relative volume of comparable pairs under semigroup majorization [0.0]
Any semigroup $mathcalS$ of matrices induces a semigroup majorization relation $precmathcalS$ on the set $Delta_n-1$ of probability $n$-vectors.<n> Pick $X,Y$ at random in $Delta_n-1$: what is the probability that $X$ and $Y$ are comparable under $precmathcalS$?
arXiv Detail & Related papers (2024-10-30T16:48:59Z) - The Communication Complexity of Approximating Matrix Rank [50.6867896228563]
We show that this problem has randomized communication complexity $Omega(frac1kcdot n2log|mathbbF|)$.
As an application, we obtain an $Omega(frac1kcdot n2log|mathbbF|)$ space lower bound for any streaming algorithm with $k$ passes.
arXiv Detail & Related papers (2024-10-26T06:21:42Z) - Asymptotic Normality of Generalized Low-Rank Matrix Sensing via Riemannian Geometry [37.53442095760427]
We prove an normality guarantee for generalized low-rank matrix sensing.<n>We parameterize the manifold of low-rank matrices by $barthetabarthetatop$.<n>We prove $sqrtn(phi0-phi*)xrightarrowDN(0,(H*)-1)$ as $ntoinfty$, where $phi0$ and $phi*$ are representations of $bartheta*$ and $barthe
arXiv Detail & Related papers (2024-07-14T15:11:13Z) - Efficient Matrix Factorization Via Householder Reflections [2.3326951882644553]
We show that the exact recovery of the factors $mathbfH$ and $mathbfX$ from $mathbfY$ is guaranteed with $Omega$ columns in $mathbfY$.
We hope the techniques in this work help in developing alternate algorithms for dictionary learning.
arXiv Detail & Related papers (2024-05-13T11:13:49Z) - Provably learning a multi-head attention layer [55.2904547651831]
Multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models.
In this work, we initiate the study of provably learning a multi-head attention layer from random examples.
We prove computational lower bounds showing that in the worst case, exponential dependence on $m$ is unavoidable.
arXiv Detail & Related papers (2024-02-06T15:39:09Z) - Characterizing Kirkwood-Dirac nonclassicality and uncertainty diagram
based on discrete Fourier transform [6.344765041827868]
We show that for the uncertainty diagram of the DFT matrix which is a transition matrix from basis $mathcal A$ to basis $mathcal B$, there is no hole"
We present that the KD nonclassicality of a state based on the DFT matrix can be completely characterized by using the support uncertainty relation.
arXiv Detail & Related papers (2023-03-30T07:55:21Z) - $\mathcal{P}\mathcal{T}$-symmetric $-g\varphi^4$ theory [0.0]
Hermitian theory is proposed: $log ZmathcalPmathcalT(g)=textstylefrac12 log Z_rm Herm(-g+rm i 0+rm i 0+$.
A new conjectural relation between the Euclidean partition functions $ZmathcalPmathcalT$-symmetric theory and $Z_rm Herm(lambda)$ of the $lambda varphi4$ is presented.
arXiv Detail & Related papers (2022-09-16T12:44:00Z) - Perturbation Analysis of Randomized SVD and its Applications to Statistics [8.731676546744353]
RSVD is a class of computationally efficient algorithms for computing the truncated SVD of large data matrices.<n>In this paper we derive upper bounds for the $ell$ and $ell_2,infty$ distances between the exact left singular vectors $widehatmathbfU$ of $widehatmathbfM$.<n>We apply our theoretical results to settings where $widehatmathbfM$ is an additive perturbation of some unobserved signal matrix $mathbfM$.
arXiv Detail & Related papers (2022-03-19T07:26:45Z) - Uncertainties in Quantum Measurements: A Quantum Tomography [52.77024349608834]
The observables associated with a quantum system $S$ form a non-commutative algebra $mathcal A_S$.
It is assumed that a density matrix $rho$ can be determined from the expectation values of observables.
Abelian algebras do not have inner automorphisms, so the measurement apparatus can determine mean values of observables.
arXiv Detail & Related papers (2021-12-14T16:29:53Z) - Spectral properties of sample covariance matrices arising from random
matrices with independent non identically distributed columns [50.053491972003656]
It was previously shown that the functionals $texttr(AR(z))$, for $R(z) = (frac1nXXT- zI_p)-1$ and $Ain mathcal M_p$ deterministic, have a standard deviation of order $O(|A|_* / sqrt n)$.
Here, we show that $|mathbb E[R(z)] - tilde R(z)|_F
arXiv Detail & Related papers (2021-09-06T14:21:43Z) - Linear Bandits on Uniformly Convex Sets [88.3673525964507]
Linear bandit algorithms yield $tildemathcalO(nsqrtT)$ pseudo-regret bounds on compact convex action sets.
Two types of structural assumptions lead to better pseudo-regret bounds.
arXiv Detail & Related papers (2021-03-10T07:33:03Z) - Sparse sketches with small inversion bias [79.77110958547695]
Inversion bias arises when averaging estimates of quantities that depend on the inverse covariance.
We develop a framework for analyzing inversion bias, based on our proposed concept of an $(epsilon,delta)$-unbiased estimator for random matrices.
We show that when the sketching matrix $S$ is dense and has i.i.d. sub-gaussian entries, the estimator $(epsilon,delta)$-unbiased for $(Atop A)-1$ with a sketch of size $m=O(d+sqrt d/
arXiv Detail & Related papers (2020-11-21T01:33:15Z) - The Average-Case Time Complexity of Certifying the Restricted Isometry
Property [66.65353643599899]
In compressed sensing, the restricted isometry property (RIP) on $M times N$ sensing matrices guarantees efficient reconstruction of sparse vectors.
We investigate the exact average-case time complexity of certifying the RIP property for $Mtimes N$ matrices with i.i.d. $mathcalN(0,1/M)$ entries.
arXiv Detail & Related papers (2020-05-22T16:55:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.