activity
20112023
most citedManopt, a Matlab toolbox for optimization on manifolds

786 citations · 2.1k across the 35 of their papers we have counts for

collaborators

39 papers

math.OC2023

Fast convergence of trust-regions for non-isolated minima via analysis of CG on indefinite matrices

Quentin Rebjock, Nicolas Boumal

Trust-region methods (TR) can converge quadratically to minima where the Hessian is positive definite. However, if the minima are not isolated, then the Hessian there cannot be pos…

math.OC2023

Open Problem: Polynomial linearly-convergent method for geodesically convex optimization?

Christopher Criscitiello, David Martínez-Rubio, Nicolas Boumal

Let be a Lipschitz and geodesically convex function defined on a -dimensional Riemannian manifold . Does there exist a first-o…

math.OC2023★ 6 cited

Benign landscapes of low-dimensional relaxations for orthogonal synchronization on general graphs

Andrew D. McRae, Nicolas Boumal

Orthogonal group synchronization is the problem of estimating elements from the orthogonal group given some relative measurements $R_{ij} \appro…

math.OC2023★ 3 cited

Fast convergence to non-isolated minima: four equivalent conditions for functions

Quentin Rebjock, Nicolas Boumal

Optimization algorithms can see their local convergence rates deteriorate when the Hessian at the optimum is singular. These singularities are inescapable when the optima are non-i…

math.OC2022★ 16 cited

The effect of smooth parametrizations on nonconvex optimization landscapes

Eitan Levin, Joe Kileel, Nicolas Boumal

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for…

math.OC2022

Computing second-order points under equality constraints: revisiting Fletcher's augmented Lagrangian

Florentin Goyens, Armin Eftekhari, Nicolas Boumal

We address the problem of minimizing a smooth function under smooth equality constraints. Under regularity assumptions on these constraints, we propose a notion of approximate firs…