6 papers
Non-normal spectral signatures of instability in neural network training dynamics
Souvik Ghosh
Training instabilities in deep networks - loss spikes, oscillatory convergence, and gradient pathologies - are empirically prevalent but lack a rigorous operator-theoretic explanat…
On anti-coproximinal and strongly anti-coproximinal subspaces of function spaces
Shamim Sohel, Souvik Ghosh, Debmalya Sain +1
The purpose of this article is to study the anti-coproximinal and strongly anti-coproximinal subspaces of the Banach space of all bounded (continuous) functions. We obtain a tracta…
On symmetricity of the norm derivatives orthogonality in operator spaces
Souvik Ghosh, Kallol Paul, Debmalya Sain
We investigate -orthogonality and its local symmetry in the space of bounded linear operators. A characterization of Hilbert space operators with symmetric numerical range is e…
Smoothness in the space of bounded linear operators on semi-Hilbert space
Somdatta Barik, Souvik Ghosh, Kallol Paul +1
Given a nonzero positive operator on a Hilbert space , a semi-inner product is naturally induced on . In this work, we introduce the notion of \emph{…
On some subspaces of vector-valued continuous function space, from the perspective of Best coapproximation
Souvik Ghosh, Kallol Paul, Debmalya Sain +1
This article explores anti-coproximinal and strongly anti-coproximinal subspaces in the spaces of vector-valued continuous functions and operator spaces. We provide a complete char…
Orthogonality induced by norm derivatives : A new geometric constant and symmetry
Souvik Ghosh, Kallol Paul, Debmalya Sain
In this article we study the difference between orthogonality induced by the norm derivatives (known as -orthogonality) and Birkhoff-James orthogonality in a normed linear spac…