2 research outputs found
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing
This paper considers reconstructing a spectrally sparse signal from a small
number of randomly observed time-domain samples. The signal of interest is a
linear combination of complex sinusoids at distinct frequencies. The
frequencies can assume any continuous values in the normalized frequency domain
. After converting the spectrally sparse signal recovery into a low rank
structured matrix completion problem, we propose an efficient feasible point
approach, named projected Wirtinger gradient descent (PWGD) algorithm, to
efficiently solve this structured matrix completion problem. We further
accelerate our proposed algorithm by a scheme inspired by FISTA. We give the
convergence analysis of our proposed algorithms. Extensive numerical
experiments are provided to illustrate the efficiency of our proposed
algorithm. Different from earlier approaches, our algorithm can solve problems
of very large dimensions very efficiently.Comment: 12 page
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees
Optimization problems with rank constraints arise in many applications,
including matrix regression, structured PCA, matrix completion and matrix
decomposition problems. An attractive heuristic for solving such problems is to
factorize the low-rank matrix, and to run projected gradient descent on the
nonconvex factorized optimization problem. The goal of this problem is to
provide a general theoretical framework for understanding when such methods
work well, and to characterize the nature of the resulting fixed point. We
provide a simple set of conditions under which projected gradient descent, when
given a suitable initialization, converges geometrically to a statistically
useful solution. Our results are applicable even when the initial solution is
outside any region of local convexity, and even when the problem is globally
concave. Working in a non-asymptotic framework, we show that our conditions are
satisfied for a wide range of concrete models, including matrix regression,
structured PCA, matrix completion with real and quantized observations, matrix
decomposition, and graph clustering problems. Simulation results show excellent
agreement with the theoretical predictions