arXiv subjects

Thomas Madden

Publications and source records attributed to Thomas Madden.

3 recordsLinked to original sources

arXiv2026

Matrix group $\Lambda$-distributions

The $\Lambda$-distribution of a compact matrix group is an invariant in algebraic probability theory that was recently introduced to study zero distributions of function field $L$-functions. It is encoded by the $\sigma$-moment generating function, a generalization of the Molien series of classical invariant theory. In this work, we compute the $\sigma$-moment generating functions of finite matrix groups in many new cases. In particular, we compute the asymptotic $\Lambda$-distributions for the infinite families of Weyl reflection groups of types $B_n/C_n$ and $D_n$, complementing the previously known case of reflection groups of type $A_n$, i.e., symmetric groups. We also establish a general result relating the shapes of $\sigma$-moment generating functions to the distributions of associated classical random variables, explaining a previous ad hoc observation for independent Gaussians arising from traces of powers on compact classical groups.

math.RT↗

arXiv2025

An accelerated proximal bundle method for convex optimization

The proximal bundle method (PBM) is a powerful and widely used approach for minimizing nonsmooth convex functions. However, for smooth objectives, its best-known convergence rate remains suboptimal, and whether PBM can be accelerated remains open. In this work, we present the first accelerated proximal bundle method that achieves the optimal $\mathscr{O}(1/\sqrt{\epsilon})$ iteration complexity for obtaining an $\epsilon$-accurate solution in smooth convex optimization. The proposed method is conceptually simple, which differs from Nesterov's accelerated gradient descent by only a single line and retains all key structural properties of the classical PBM. In particular, it relies on the same minimal assumptions on model approximations and preserves the standard bundle testing criterion. Numerical experiments confirm the accelerated $\mathscr{O}(1/\sqrt{\epsilon})$ convergence rate predicted by our theory.

math.OC↗

arXiv2011

Including robustness in multi-criteria optimization for intensity-modulated proton therapy

We present a method to include robustness into a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives as well as the trade-off between robustness and nominal plan quality. In MCO, a database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. In our approach, robustness is integrated into MCO by adding robustified objectives and constraints to the MCO problem. Uncertainties of the robust problem are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios. A robustified objective represents the worst objective function value that can be realized for any of the error scenarios. The optimization method is based on a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. A base-of-skull case is used to demonstrate the robust optimization method. It is demonstrated that the robust optimizationmethod reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved tradeoffs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The latter illustrates the advantage of MCO in the context of robust planning. For all cases examined, the robust optimization for each Pareto optimal plan takes less than 5 min on a standard computer, making a computationally friendly interface possible to the planner.

physics.med-ph↗