6 citations · 16 across the 10 of their papers we have counts for
5 papers · 1 filter
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
Gavin Zhang, Salar Fattahi, Richard Y. Zhang
In practical instances of nonconvex matrix factorization, the rank of the true solution is often unknown, so the rank of the model can be overspecified as $r>r^{\st…
Complexity of Chordal Conversion for Sparse Semidefinite Programs with Small Treewidth
Richard Y. Zhang
If a sparse semidefinite program (SDP), specified over matrices and subject to linear constraints, has an aggregate sparsity graph with small treewidth, then ch…
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent
Gavin Zhang, Hong-Ming Chiu, Richard Y. Zhang
Non-convex gradient descent is a common approach for estimating a low-rank ground truth matrix from noisy measurements, because it has per-iteration costs as low as $O(…
Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterization
Richard Y. Zhang
We consider minimizing a twice-differentiable, -smooth, and -strongly convex objective over an positive semidefinite matrix , under the assumption…
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
Gavin Zhang, Salar Fattahi, Richard Y. Zhang
We consider using gradient descent to minimize the nonconvex function over an factor matrix , in which is an underlying smooth convex cost funct…