no code implementations • 30 Sep 2023 • Kushal Chakrabarti, Mayank Baranwal
This paper considers solving distributed convex optimization problems in peer-to-peer multi-agent networks.
no code implementations • 7 Dec 2022 • Mayank Baranwal, Param Budhraja, Vishal Raj, Ashish R. Hota
Gradient-based first-order convex optimization algorithms find widespread applicability in a variety of domains, including machine learning tasks.
no code implementations • 5 Dec 2022 • Anandsingh Chauhan, Mayank Baranwal, Ansuma Basumatary
Power grids, across the world, play an important societal and economical role by providing uninterrupted, reliable and transient-free power to several industries, businesses and household consumers.
no code implementations • 2 Mar 2022 • Hardik Meisheri, Somjit Nath, Mayank Baranwal, Harshad Khadilkar
Through empirical evaluations, it is further shown that the inventory management with uncertain lead times is not only equivalent to that of delay in information sharing across multiple echelons (\emph{observation delay}), a model trained to handle one kind of delay is capable to handle delays of another kind without requiring to be retrained.
no code implementations • 2 Dec 2021 • Param Budhraja, Mayank Baranwal, Kunal Garg, Ashish Hota
We achieve this by first leveraging a continuous-time framework for designing fixed-time stable dynamical systems, and later providing a consistent discretization strategy, such that the equivalent discrete-time algorithm tracks the optimizer in a practically fixed number of iterations.
1 code implementation • 17 Aug 2021 • Somjit Nath, Mayank Baranwal, Harshad Khadilkar
Several real-world scenarios, such as remote control and sensing, are comprised of action and observation delays.
no code implementations • 13 Feb 2020 • Abram Magner, Mayank Baranwal, Alfred O. Hero III
We investigate the power of GCNs, as a function of their number of layers, to distinguish between different random graph models on the basis of the embeddings of their sample graphs.
no code implementations • 28 Oct 2019 • Abram Magner, Mayank Baranwal, Alfred O. Hero III
We give a precise characterization of the set of pairs of graphons that are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph.
no code implementations • 31 Oct 2018 • Amber Srivastava, Mayank Baranwal, Srinivasa Salapaka
Typically clustering algorithms provide clustering solutions with prespecified number of clusters.
no code implementations • 14 Apr 2016 • Mayank Baranwal, Brian Roehl, Srinivasa M. Salapaka
This paper presents a novel and efficient heuristic framework for approximating the solutions to the multiple traveling salesmen problem (m-TSP) and other variants on the TSP.