438 citations · 1k across the 57 of their papers we have counts for
7 papers · 1 filter
Dynamic cut aggregation in L-shaped algorithms
Martin Biel, Mikael Johansson
We present a novel framework for dynamic cut aggregation in L-shaped algorithms. The aim is to improve the parallel performance of distributed L-shaped algorithms through reduced c…
Anderson Acceleration of Proximal Gradient Methods
Vien V. Mai, Mikael Johansson
Anderson acceleration is a well-established and simple technique for speeding up fixed-point computations with countless applications. Previous studies of Anderson acceleration in…
Efficient Stochastic Programming in Julia
Martin Biel, Mikael Johansson
We present StochasticPrograms.jl, a user-friendly and powerful open-source framework for stochastic programming written in the Julia language. The framework includes both modeling…
Compressed Gradient Methods with Hessian-Aided Error Compensation
Sarit Khirirat, Sindri Magnússon, Mikael Johansson
The emergence of big data has caused a dramatic shift in the operating regime for optimization algorithms. The performance bottleneck, which used to be computations, is now often c…
Noisy Accelerated Power Method for Eigenproblems with Applications
Vien V. Mai, Mikael Johansson
This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex…
Curvature-Exploiting Acceleration of Elastic Net Computations
Vien V. Mai, Mikael Johansson
This paper introduces an efficient second-order method for solving the elastic net problem. Its key innovation is a computationally efficient technique for injecting curvature info…
Harnessing the Power of Serverless Runtimes for Large-Scale Optimization
Arda Aytekin, Mikael Johansson
The event-driven and elastic nature of serverless runtimes makes them a very efficient and cost-effective alternative for scaling up computations. So far, they have mostly been use…