
handle: 20.500.12395/28414
Some majorization inequalities on real vectors are provided and applied to derive some inequalities concerning norm, eigenvalues, singular values and traces of matrices. For a vector \(x=(x_1,x_2,\dots,x_n)\in{\mathbb R}^n\) one denotes by \(x^{\downarrow}=(x^{\downarrow}_1,x^{\downarrow}_2,\dots,x^{\downarrow}_n)\) the vector having the components of \(x\) rearranged in decreasing order. For \(x=(x_1,x_2,\dots,x_n)\) and \(y=(y_1,y_2,\dots,y_n)\) one says that \(x\) is weakly majorized by \(y\) and one denotes \(x\prec_wy\) if \(\sum_{i=1}^kx^{\downarrow}_i\leq\sum_{i=1}^ky^{\downarrow}_i\), \(\forall k=1,2,\dots,n\). If \(x\) is weakly majorized by \(y\) and \(\sum_{i=1}^kx^{\downarrow}_i=\sum_{i=1}^ky^{\downarrow}_i\) then one says that \(x\) is majorized by \(y\) and one denotes \(x\prec y\). By replacing the sums \(\sum\) by the products \(\prod\) one obtains the definitions of \(x\prec_{wlog}y\) and \(x\prec_{log}y\) (respectively \(x\) is weakly log majorized by \(y\) and \(x\) is log majorized by \(y\). Some majorization inequalities for vectors are provided, for instance {\parindent=6mm \begin{itemize}\item[i)] \(\frac{1}{m}(x_1,x_2,\dots,x_m)\prec(r_1y_1,r_2y_2,\dots,r_my_m)\) if \(x_i,y_i\in{\mathbb R}^n\) and \(x_i\prec r_1y_1+r_2y_2+\cdots+r_my_m\), \(i=1,2,\dots m\). \item[ii)] \(\frac{1}{m}(x_1,x_2,\dots,x_m)\prec_w(r_1y_1,r_2y_2,\dots,r_my_m)\) if \(x_i,y_i\in{\mathbb R}^n_+\) and \(x_i\prec_wy_1^{r_1}\circ y_2^{r_2}\circ\cdots\circ y_m^{r_m}\), \(i=1,2,\dots m\), where \(\circ\) denotes the componentwise product of two vectors. \end{itemize}} The special case \(m=2\) is applied to generate many matrix inequalities, for example: \((\sigma(\frac{A+B}{2}),(\sigma(\frac{A+B}{2}))\prec_w(\sigma(A),(\sigma(B))\), \((\sigma(AB),(\sigma(AB))\prec_{log}(\sigma^2(A),(\sigma^2(B))\), \(\frac{1}{2}\|(A+B)\oplus(A+B)\|\leq\|A\oplus B\|\), \(\|(AB)\oplus(AB)\|\leq\|A^*A\oplus B^*B\|\), \(\|(A\circ B)\oplus(A\circ B)\|\leq\|A^*A\oplus B^*B\|\), where for \(A\in M_n\), \(\sigma(A)\) denotes the singular value vector of A with the singular values arranged in decreasing order. A generalization and a different proof is provided for a recent result on majorization, concerning partitioned Hermitian positive semidefinite matrices.
Eigenvalues, singular values, and eigenvectors, unitarily invariant norm, Eigenvalue, matrix inequality, Hermitian matrix, matrix norm, Matrix inequality, Hermitian matrices, Norms of matrices, numerical range, applications of functional analysis to matrix theory, Discrete Mathematics and Combinatorics, Hermitian, skew-Hermitian, and related matrices, Majorization, trace, Numerical Analysis, Algebra and Number Theory, eigenvalues, positive semidefinite matrices, singular value, Unitarily invariant norm, Inequalities involving eigenvalues and eigenvectors, Positive semidefinite matrix, Singular value, Matrix norm, Miscellaneous inequalities involving matrices, majorization, Geometry and Topology, Mathematics
Eigenvalues, singular values, and eigenvectors, unitarily invariant norm, Eigenvalue, matrix inequality, Hermitian matrix, matrix norm, Matrix inequality, Hermitian matrices, Norms of matrices, numerical range, applications of functional analysis to matrix theory, Discrete Mathematics and Combinatorics, Hermitian, skew-Hermitian, and related matrices, Majorization, trace, Numerical Analysis, Algebra and Number Theory, eigenvalues, positive semidefinite matrices, singular value, Unitarily invariant norm, Inequalities involving eigenvalues and eigenvectors, Positive semidefinite matrix, Singular value, Matrix norm, Miscellaneous inequalities involving matrices, majorization, Geometry and Topology, Mathematics
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