Abstract:We propose the sketched landing method, a randomized variant of the landing method for optimization under orthogonality constraints. Each landing step consists of the sum of a normal component, which reduces infeasibility, and a tangent component, which decreases the objective function. Our main contribution is the introduction of low-dimensional random sketch matrices to reduce the computational cost of these directions. We consider both dense (Gaussian) and sparse (subsampling) sketch matrices, and show how they reduce the per-iteration cost while preserving convergence guarantees in expectation.
From: Florentin Goyens [view email]
[v1]
Fri, 29 May 2026 16:25:34 UTC (713 KB)
[v2]
Fri, 24 Jul 2026 08:01:11 UTC (715 KB)
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