most citedOptimization over bounded-rank matrices through a desingularization enables joint global and local guarantees

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

math.OC2026★ 1 cited

Optimization over bounded-rank matrices through a desingularization enables joint global and local guarantees

Quentin Rebjock, Nicolas Boumal

Convergence guarantees for optimization over bounded-rank matrices are delicate to obtain because the feasible set is a nonsmooth and nonconvex algebraic variety. Existing techniqu…

math.OC2026

Smooth, globally Polyak-Łojasiewicz functions are nonlinear least-squares

Nicolas Boumal, Christopher Criscitiello, Quentin Rebjock

The Polyak-Łojasiewicz (PŁ) condition is often invoked in nonconvex optimization because it allows fast convergence of algorithms beyond strong convexity. A function $f \colon \m…

math.OC2026

A practical randomized trust-region method to escape saddle points in high dimension

Radu-Alexandru Dragomir, Xiaowen Jiang, Bonan Sun +1

Without randomization, escaping the saddle points of requires at least pieces of information about (values, gradients, Hessian-ve…

math.OC2026

Sensor network localization has a benign landscape after low-dimensional relaxation

Christopher Criscitiello, Andrew D. McRae, Quentin Rebjock +1

We consider the sensor network localization problem, which is closely related to multidimensional scaling and Euclidean distance matrix completion. Given a ground truth configurati…

math.OC2026

Synchronization on circles and spheres with nonlinear interactions

Christopher Criscitiello, Quentin Rebjock, Andrew D. McRae +1

We consider the dynamics of points on a sphere in () which attract each other according to a function of their inner products. When is linear (…