18 citations · 267 across the 76 of their papers we have counts for
6 papers · 2 filters
Speeding up the Goemans-Williamson randomized procedure by difference-of-convex optimization
Hadi Salloum, Roland Hildebrand, Nhat Trung Nguyen +4
We present a novel approach to accelerate the Goemans-Williamson (GW) randomized rounding procedure for quadratic unconstrained binary optimization (QUBO) problems. Instead of solv…
Adaptive Regularized Newton Method with Inexact Hessian
Aleksandr Shestakov, Nail Bashirov, Andrei Semenov +4
Newton's method is the most widespread high-order method, demanding the gradient and the Hessian of the objective function. However, one of the main disadvantages of Newtons method…
Dual Smoothing for Decentralized Optimization
Alexander Rogozin, Nhat Trung Nguyen, Hamed Azami Zenuzagh +1
Decentralized optimization is widely used in different fields of study such as distributed learning, signal processing, and various distributed control problems. In these types of…
A Parameter-free Decentralized Algorithm for Composite Convex Optimization
Xiaokai Chen, Ilya Kuruzov, Gesualdo Scutari +1
The paper studies decentralized optimization over networks, where agents minimize a composite objective consisting of the sum of smooth convex functions--the agents' losses--and an…
Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
Maxim Divilkovskiy, Alexander Gasnikov
We study non-smooth stochastic decentralized optimization problems over time-varying networks, where objective functions are distributed across nodes and network connections may in…
Adaptive Stepsize Selection in Decentralized Convex Optimization
Ilya Kuruzov, Xiaokai Chen, Gesualdo Scutari +1
We study decentralized optimization where multiple agents minimize the average of their (strongly) convex, smooth losses over a communication graph. Convergence of the existing dec…