Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
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A nonlinear observer on SL(3) achieves local exponential convergence for homography estimation by minimizing an image-intensity cost function with explicit non-degeneracy conditions.
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Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
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Equivariant Observer Design on SL(3) for Image Intensity-Based Homography Estimation
A nonlinear observer on SL(3) achieves local exponential convergence for homography estimation by minimizing an image-intensity cost function with explicit non-degeneracy conditions.