Panos Parpas

I am a Reader in Computational Optimization and a member of the the Computational Optimisation Group at the Department of Computing, Imperial College London. Before joining Imperial College I was a postdoctoral fellow at the MIT Energy Initiative (2009-2011). Before that I was a quantitative associate at Credit-Suisse (2007-2009).

Research Interests

I am interested in the development and analysis of algorithms for large scale optimisation problems. I am also interested in exploiting the structure of large scale models arising in applications. Below is a list of my current research projects.

• Multi-resoultion algorithms in Machine Learning
• Or watch a recent talk for an overview of multi-resoultion algorithms in ML
• Stability of optimization algorithms
• Optimization methods in finance
• Perturbation methods and algorithms for Markov Processes
• Sampling algorithms for Stochastic Stochastic Dual Dynamic Programming.

Recent Publications

• J. S. Campos Salazar, P. Parpas. A Multigrid approach to SDP relaxations of sparse polynomial optimization problems SIAM Journal on Optimization, September 2018.
• P. Parpas. A Multilevel Proximal Gradient Algorithm for Large Scale Optimization , SIAM Journal on Scientific Computing, Vol. 39, Issue 5, Nov. 2017.
• V. Hovhannisyan, P. Parpas, and S. Zafeiriou. MAGMA: Multi-level accelerated gradient mirror descent algorithm for large-scale convex composite minimization , SIAM Journal on Imaging Sciences, 9(4), 1829–1857, 2016.
• R. Baltean-Lugojan, and P. Parpas. Robust Numerical Calibration for Implied Volatility Expansion Models , SIAM Journal on Financial Mathematics, 2016, 7(1), 917–946.