4 citations · 8 across the 8 of their papers we have counts for
10 papers
Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
In maximum-likelihood quantum state tomography, both the sample size and dimension grow exponentially with the number of qubits. It is therefore desirable to develop a stochastic f…
Two Polyak-Type Step Sizes for Mirror Descent
Jun-Kai You, Yen-Huan Li
We propose two Polyak-type step sizes for mirror descent and prove their convergences for minimizing convex locally Lipschitz functions. Both step sizes, unlike the original Polyak…
Maximum-Likelihood Quantum State Tomography by Cover's Method with Non-Asymptotic Analysis
Chien-Ming Lin, Hao-Chung Cheng, Yen-Huan Li
We propose an iterative algorithm that computes the maximum-likelihood estimate in quantum state tomography. The optimization error of the algorithm converges to zero at an $O ( (…
A General Convergence Result for Mirror Descent with Armijo Line Search
Yen-Huan Li, Carlos A. Riofrio, Volkan Cevher
Existing convergence guarantees for the mirror descent algorithm require the objective function to have a bounded gradient or be smooth relative to a Legendre function. The bounded…
Learning-Based Compressive MRI
Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li +4
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling pat…
Convergence of the Exponentiated Gradient Method with Armijo Line Search
Yen-Huan Li, Volkan Cevher
Consider the problem of minimizing a convex differentiable function on the probability simplex, spectrahedron, or set of quantum density matrices. We prove that the exponentiated g…