5 papers · 1 filter
Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry
Susmit Sarkar, Abhinav Raghuvanshi, Kushal Chakrabarti +1
We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized stochastic primal…
On a Gradient Approach to Chebyshev Center Problems with Applications to Function Learning
Abhinav Raghuvanshi, Mayank Baranwal, Debasish Chatterjee
We introduce , the first gradient-based optimization framework for solving Chebyshev center problems, a fundamental challenge in optimal function learning and geom…
On Linear Convergence of Distributed Stochastic Bilevel Optimization over Undirected Networks via Gradient Aggregation
Ajay Tak, Mayank Baranwal
Many large-scale constrained optimization problems can be formulated as bilevel distributed optimization tasks over undirected networks, where agents collaborate to minimize a glob…
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality
Kushal Chakrabarti, Mayank Baranwal
This paper considers solving distributed optimization problems in peer-to-peer multi-agent networks. The network is synchronous and connected. By using the proportional-integral (P…
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates
Mayank Baranwal, Kushal Chakrabarti
Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization. We introduce a second-order primal--du…