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math.OC2026
Doubling the dimension yields a benign landscape for the squared-stress
Christopher Criscitiello
We consider the Euclidean distance geometry problem (EDG): given a subset of the pairwise distances of an unknown cloud of points in , recover the point cloud…
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 \mat…
math.OC2026
Negative curvature obstructs the existence of good barriers for interior-point methods
Christopher Criscitiello, Harold Nieuwboer, Michael Walter
Interior-point methods (IPMs) are a cornerstone of Euclidean convex optimization, due to their strong theoretical guarantees and practical performance. Motivated by scaling problem…