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Posted September 21, 2026

Control and Optimization Seminar Questions or comments?

Tim Hoheisel, McGill University, Canada
A Computational Framework for Linear Inverse Problems via the Maximum Entropy on the Mean Method

We present a framework for solving linear inverse problems that is computationally tractable and has mathematical certificates. To this end, we interpret the ground truth of a linear inverse problem as a random vector with unknown distribution. We solve for a distribution which is close to a prior P (guessed or data-driven) measured in the KL-divergence while also having an expectation that yields high fidelity with the given data that defines the problem. After reformulation this yields a strictly convex, finite dimensional optimization problem whose regularizer, the MEM functional, is paired in duality with the log-moment generating function of the prior P. We exploit this computationally via Fenchel-Rockafellar duality. When no obvious guess for P is available, we use data to generate an empirical prior. Using techniques from variational analysis and stochastic optimization, we show that, and at what rate, the solution of the empirical problems converges (as the sample size grows) to the solution of the problem with known prior. Moreover, we show how stochastic subgradient methods can be brought to bear on the MEM dual. This is joint work with Rustum Choksi (McGill), Matthew King-Roskamp (McGill), and Gabriel Rioux (Imperial College).

Posted September 21, 2026

Control and Optimization Seminar Questions or comments?

Radu Bot, University of Vienna
TBA

Posted September 22, 2026

Control and Optimization Seminar Questions or comments?

Rene Henrion, Weierstrass Institute for Applied Analysis and Stochastics, Germany
TBA

Posted September 1, 2026

Control and Optimization Seminar Questions or comments?

Jun Liu, University of Waterloo, Canada
TBA