Quantum Frank-Wolfe algorithms reduce dimension dependence in sparse convex optimization, from O(d) to O(sqrt d) function queries for vectors and from O(d^2) to O(d) per update step for matrices under certain assumptions.
Quantum algorithms for svd-based data representation and analysis
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Quantum Algorithms for Projection-Free Sparse Convex Optimization
Quantum Frank-Wolfe algorithms reduce dimension dependence in sparse convex optimization, from O(d) to O(sqrt d) function queries for vectors and from O(d^2) to O(d) per update step for matrices under certain assumptions.