Extends variable projection to constrained separable nonlinear least-squares via bilevel collapse, yielding exact reduced gradients and a convergent conditional-gradient algorithm.
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2026 2verdicts
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GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.
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Constrained Variable Projection for Structured Problems
Extends variable projection to constrained separable nonlinear least-squares via bilevel collapse, yielding exact reduced gradients and a convergent conditional-gradient algorithm.
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Group-Aware Matrix Estimation and Latent Subspace Recovery
GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.