Abstract:We first present an inequality for the Frobenius norm of the Hadamard product of two any square matrices and positive semidefinite matrices. Then, we obtain a trace inequality for products of two positive semidefinite block matrices by using 2 × 2 block matrices.
“…If A and B are positive semi-definite matrices, then 0 ≤ Tr(A B) ≤ Tr(A)Tr(B) [10]. Defining A ≡ b true 1 (b true 1 ) TF and B ≡F shows that the inequality in Eq.…”
This paper derives an algorithm to determine the approximate attitude of a vehicle from both vector and arc-length observations, which are the most general types of attitude observations. It is assumed that one of the vector observations is more accurate than the other vector and arc-length observations. The solution is found by solving a quartic polynomial equation. Then the quaternion can be determined from the polynomial solution. The attitude errorcovariance is also derived using both an attitude perturbation approach and a constrained least squares approach. Both are shown to yield identical results. An optimality condition is also derived that compares the derived suboptimal error-covariance with the optimal one. Several special cases, such as a set of one direction observation and an arc-length observation, are shown. Simulation results using a Monte Carlo analysis are shown to verify the derived algorithm.
“…If A and B are positive semi-definite matrices, then 0 ≤ Tr(A B) ≤ Tr(A)Tr(B) [10]. Defining A ≡ b true 1 (b true 1 ) TF and B ≡F shows that the inequality in Eq.…”
This paper derives an algorithm to determine the approximate attitude of a vehicle from both vector and arc-length observations, which are the most general types of attitude observations. It is assumed that one of the vector observations is more accurate than the other vector and arc-length observations. The solution is found by solving a quartic polynomial equation. Then the quaternion can be determined from the polynomial solution. The attitude errorcovariance is also derived using both an attitude perturbation approach and a constrained least squares approach. Both are shown to yield identical results. An optimality condition is also derived that compares the derived suboptimal error-covariance with the optimal one. Several special cases, such as a set of one direction observation and an arc-length observation, are shown. Simulation results using a Monte Carlo analysis are shown to verify the derived algorithm.
“…For some classical trace inequalities see [4], [6], [30] and [38], which are continuations of the work of Bellman [2]. For related works the reader can refer to [1], [3], [4], [14], [22], [27], [28], [31] and [35].…”
Abstract. By the use of the celebrated Kato's inequality we obtain in this paper some new inequalities for trace class operators on a complex Hilbert space H. Natural applications for functions defined by power series of normal operators are given as well.
“…For some classical trace inequalities see [20][21][22][23][24], which are continuations of the work of Bellman [25]. For related works the reader can refer to [20,24,[26][27][28][29][30][31][32].…”
A new quantum f -divergence for trace class operators in Hilbert Spaces is introduced. It is shown that for normalised convex functions it is nonnegative. Some upper bounds are provided. Applications for some classes of convex functions of interest are also given.
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