We develop, analyze and implement numerical algorithms to solve
optimization problems of the form: min f(x) where x is a point on a
smooth manifold. To this end, we first study differential and Riemannian
geometry (with a focus dictated by pragmatic concerns). We also discuss
several applications.
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701 - Geodesic convexity: why, and what we need
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112.2 - Comparing tangent vectors: parallel…
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702.2 - Geodesic convexity - Basic facts and more…
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702.1 - Geodesic convexity - Basic definitions
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503.3 - Tangent vectors without embedding space -…
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503.2 - Tangent vectors without embedding space -…
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503.1 - Tangent vectors without embedding space -…
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502.3 - Smooth sets and functions - Smooth…
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502.2 - Smooth sets and functions - Example:…
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502.1 - Smooth sets and functions - Charts and…
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501.2 - From embedded to general manifolds - How,…
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501.1 - From embedded to general manifolds - Why?
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213.2 - Linear convergence with…
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213.1 - Linear convergence with…
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114.2 - Distance, geodesics and complete…
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