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Found 10 papers, 1 papers with code
Linear Convergence of Pre-Conditioned PI Consensus Algorithm under Restricted Strong Convexity
This paper considers solving distributed convex optimization problems in peer-to-peer multi-agent networks.
Generalized Gradient Flows with Provable Fixed-Time Convergence and Fast Evasion of Non-Degenerate Saddle Points
Gradient-based first-order convex optimization algorithms find widespread applicability in a variety of domains, including machine learning tasks.
PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks
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.
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management
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.
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows
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.
Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays
Several real-world scenarios, such as remote control and sensing, are comprised of action and observation delays.
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version
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.
Fundamental Limits of Deep Graph Convolutional Networks
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.
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset
Typically clustering algorithms provide clustering solutions with prespecified number of clusters.
A Deterministic Annealing Approach to the Multiple Traveling Salesmen and Related Problems
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.
