Loewner's theorem in several variables

We try to find a continuous functionu defined on a real right half-line with the range (0, ∞) such thatu −1 is operator monotone. We then look for another functionv such thatv(u −1) is operator monotone, namely,u(A)≦u(B) impliesv(A)≦v(B) for self-adjoint operatorsA andB.

A capital letter means a bounded liner operator on a complex Hilbert space H , and B(H) denotes the algebra of all bounded linear operators on H equipped with operator norm ‖ · ‖ . An operator T is said to be positive (denoted by T 0) if (Tx,x) 0 for all x∈H and an operator T is said to be strictly positive (denoted by T > 0) if T is positive and invertible. Chaotic order is defined by logA logB for strictly positive operators A and B . A continuous real-valued function f defined on an interval J is called operator monotone if A B implies that f (A) f (B) for all self-adjoint operators A , B with spectra in J . The well known celebrated Lower-Heinz inequality asserts that if A B 0, then Aα Bα for any α ∈ [0,1] , which means that t → tα is operator monotone. Another well known example of operator monotone function is t → log t on (0,∞) . Recently, Furuta [5] showed that if A > B , then f (Aα ) > f (Bα) for all α ∈ (0,1] and if logA > logB , then there exists β ∈ (0,1] such that f (Aα) > f (Bα) for all α ∈ (0,β ] , where f (t) is a non-constant operator monotone function on [0,∞) . Comprehensive survey on related order preserving operator inequalities were given in [2,4]. In this paper, we will obtain an improvement which leads to the result of Moslehian and Najafi [6].

A short proof is given based on C*-algebra theory for the well-known theorem that if S and Tare bounded selfadjoint operators on a Hubert space such that 0^5^T then Sai|7a for each0^a 0; and S+e/and T+el are both invertible. Since (S+eI)x converges to Sx in norm when e-0 for each a>0, and since the positive operators in B(%>) form a norm closed set, it suffices to prove the theorem assuming that S and T are invertible. (The case a=0 can be verified directly, since S° is the range projection of 5.) Let E denote the set of exponents a in [0, 1] for which the function t—t is operator monotone. Trivially 0e£ and 1 eE. Since the function a-S is continuous from [0, 1] to F(§) in the norm topology we see that Fis a closed set. The proof will be complete when we show that Fis convex. Take a and s in E. Then S°^TX; hence r^^F72^/. It follows that \\S/2T-'*\\£l. Similarly \\Ss'2T-s/2\\^\. With P(A) the spectral radius of an operator A we have p(AB) = p(BA). Therefore /j*-U+s)/lMa+s)/2j--<+s1li) _ D('r'.a-s)li'j—iix+s'iHc{a+s)lij-l.!i+s)H-Y-t.!ii-s)li _ „(T—f/!c(»+j!)/2T-a/2 <; II j—0/2c(«t+/»/2<T--«/2t| <; \\T-pl2Ssl2 ||S°,/2T_,I/21| 1. Received by the editors April 27, 1972. AMS 1970 subject classifications. Primary 47B15; Secondary 46L05.

An operator monotone function [formula omitted] and strictly chaotic order

High-resolution methods are generally defined to be high-performance methods for estimating and/or detecting the desired and/or undesired signal components present in a given set of data. The term “high-resolution” also implies a good ability to resolve very “similar” signal components. One of the most common problems in signal processing is known as frequency estimation. In frequency estimation, “high-resolution” often refers to a good ability to resolve two or more closely located frequencies in the given data. There are two groups of high-resolution methods. One is parametric methods, and the other non-parametric methods. The parametric high-resolution methods result from ingenious exploitations of known data structures. The non-parametric high-resolution methods maximize the output of some desired information with little knowledge of the data structure. The choice between parametric methods and non-parametric methods largely depends on one’s confidence in the assumed data model. In this chapter, we expose the readers to a range of existing parametric high-resolution methods. In Section 1.2, we present several frequency estimation techniques using algebraic principles. They are linear prediction, matrix pencil, and iterative quadratic maximum likelihood. The linear prediction method and the matrix pencil method can achieve near-optimal accuracy of estimation without the local convergence issues associated with the optimal methods. The computational complexities of the two methods are among the most efficient. The iterative quadratic maximum likelihood method is an approximation of the (exact) maximum likelihood method. Under some condition (e.g., high SNR), this approximation achieves the optimal accuracy. Concepts like forwardand-backward averaging, total least square, and (joint) singular value decomposition of orthonormal matrices will also be discussed. In Section 1.3, we present methods that exploit large sample theorems in statistics. In particular, we focus on data of multiple independent measurements. The key data structure is captured by the dominant (principal) subspace of the data matrix or the dominant eigenvectors of the data covariance matrix. The principal subspace is referred

The main aim of this survey article is to present recent developments of matrix versions of the arithmetic–geometric mean inequality. Among others, we show improvements and generalizations of the arithmetic–geometric mean inequality for unitarily invariant norms via the Hadamard product, and for singular values via the operator monotone functions.

Let $A, B$ be positive operators and let f be any operator monotone function. We obtain inequalities for $|||f(A)X - Xf(B)|||$ in terms of $|||f\left(|AX - XB| \right) |||$ for every unitarily invariant norm. The case $X = I$ was considered by T. Ando [Math. Z., 197 (1988), pp. 403--409], and some of our results reduce to his results in this special case. Some related inequalities are obtained.

The subject is the applications of the use of quasi-entropy in finite dimensional spaces to many important quantities in quantum information. Operator monotone functions and relative modular operators are used. The origin is the relative entropy, and the f -divergence, monotone metrics, covariance and the \chi^2 -divergence are the most important particular cases. The extension of monotone metrics to those with two parameters is a new concept. Monotone metrics are also characterized by their joint convexity property.

We study the Moore–Penrose inverse of perturbations by a proper symmetrically-normed ideal of a closed range operator on a Hilbert space. We show that the notion of essential codimension of projections gives a characterization of subsets of such perturbations in which the Moore–Penrose inverse is continuous with respect to the metric induced by the operator ideal. These subsets are maximal satisfying the continuity property, and they carry the structure of real analytic Banach manifolds, which are acted upon transitively by the Banach–Lie group consisting of invertible operators associated with the ideal. This geometric construction allows us to prove that the Moore–Penrose inverse is indeed a real bianalytic map between infinite-dimensional manifolds. We use these results to study the polar decomposition of closed range operators from a similar geometric perspective. At this point we prove that operator monotone functions are real analytic in the norm of any proper symmetrically-normed ideal. Finally, we show that the maps defined by the operator modulus and the polar factor in the polar decomposition of closed range operators are real analytic fiber bundles.

The geometric separability probability of composite quantum systems has been extensively studied in the recent decades. One of the simplest but strikingly difficult problem is to compute the separability probability of qubit–qubit and rebit–rebit quantum states with respect to the Hilbert–Schmidt measure. A lot of numerical simulations confirm the and conjectured probabilities. We provide a rigorous proof for the separability probability in the real case and we give explicit integral formulas for the complex and quaternionic case. Milz and Strunz studied the separability probability with respect to given subsystems. They conjectured that the separability probability of qubit–qubit (and qubit-qutrit) states of the form of depends on (on single qubit subsystems), moreover it depends only on the Bloch radii (r) of D and it is constant in r. Using the Peres–Horodecki criterion for separability we give a mathematical proof for the probability and we present an integral formula for the complex case which hopefully will help to prove the probability, too. We prove Milz and Strunz’s conjecture for rebit–rebit and qubit–qubit states. The case, when the state space is endowed with the volume form generated by the operator monotone function is also studied in detail. We show that even in this setting Milz and Strunz’s conjecture holds true and we give an integral formula for separability probability according to this measure.

Let $\sigma$ be an operator mean and $f$ a non-constant operator monotone function on $(0,\infty)$ associated with $\sigma$. If operators $A, B$ satisfy $0\le A \le B$, then it holds that $Y \sigma (tA+X) \le Y \sigma (tB+X)$ for any non-negative real number $t$ and any positive, invertible operators $X,Y$. We show that the condition $ Y \sigma (tA+X) \le Y \sigma (tB+X)$ for a sufficiently small $t>0$ implies $A \le B$ if and only if $X$ is a positive scalar multiple of $Y$ or the associated operator monotone function $f$ with $\sigma$ has the form $f(t) = (at+b)/(ct+d)$, where $a,b,c,d$ are real numbers satisfying $ad-bc>0$.

In this paper we prove a lower bound for the determinant of the covariance matrix of quantum mechanical observables, which was conjectured by Gibilisco et al. and has the interpretation of uncertainty. The lower bound is given in terms of the commutator of the state and the observables and quantum Fisher information (generated by an operator monotone function).

We investigate the correspondence between operator monotone functions and Stieltjes functions. Then we shall prove that the class of nonnegative operator monotone functions is closed under certain operations, and applying those we decide the case when some binary operations become operator means.

We discuss multivariate functions that can be represented as the Schur complement of a linear (matrix-, tensor-, or operator-valued) pencil, i.e., the class of Bessmertnyi realizable functions. We motivate this by showing that for multiphase composites, both the effective operator in the theory of composites as well as the DtN map for an electrical network are in this class in which the associated linear pencil is of positive semidefinite type. This naturally leads to multivariate (matrix-, tensor-, or operator-valued) Herglotz-Nevanlinna functions in this class and several open problems in realizability theory. Next, we present Bessmertnyi realizations as the ``universal" state-space model/realization [compared to others common in electrical engineering (e.g., Kalman-type, Fornasini-Marchesini, Givone-Roesser) that are just a special cases of the Bessmertnyi realization]. Then we discuss our recent work (see doi:10.1007/s11785-021-01150-2 and doi:10.1016/j.laa.2021.06.007) on extensions of the Bessmertnyi realization theorem for multivariate rational functions, Schur complement algebra and operations, and their application in symmetric determinantal representations of polynomials. Finally, we will show how the latter relates to the open realization problems above. This is based on joint work with Anthony Stefan (Florida Institute of Technology).

Effective frequency estimation has become one of the great challenges in power systems especially in future smart grid systems due to their varying topology and tenable supply-demand mismatch. In this paper, we have presented a Matrix pencil method (MPM) based technique for real-time frequency estimation in balanced and unbalanced power systems by exploiting widely linear model of power system. Comparison of this method was performed with recently developed CLMS and ACLMS techniques based on strictly and widely linear modeling respectively. It was observed that MPM demonstrates significantly better estimation accuracy and time resolution compared to these techniques.