no code implementations • 10 Mar 2024 • Ziye Ma, Ying Chen, Javad Lavaei, Somayeh Sojoudi
Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions.
no code implementations • 15 Feb 2023 • Ziye Ma, Igor Molybog, Javad Lavaei, Somayeh Sojoudi
This paper studies the role of over-parametrization in solving non-convex optimization problems.
no code implementations • 15 Aug 2022 • Baturalp Yalcin, Ziye Ma, Javad Lavaei, Somayeh Sojoudi
In this paper, we shed light on some major differences between these two methods.
1 code implementation • 8 Mar 2022 • Ziye Ma, Somayeh Sojoudi
We prove that as long as the RIP constant of the noiseless objective is less than $1/3$, any spurious local solution of the noisy optimization problem must be close to the ground truth solution.
no code implementations • 18 May 2021 • Ziye Ma, Yingjie Bi, Javad Lavaei, Somayeh Sojoudi
By analyzing the landscape of the non-convex problem, we first propose a global guarantee on the maximum distance between an arbitrary local minimizer and the ground truth under the assumption that the RIP constant is smaller than $1/2$.
no code implementations • 22 Jan 2021 • Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi
We extend the analysis to the SDP, where the feasible set geometry is exploited to design a branching scheme that minimizes the worst-case SDP relaxation error.
no code implementations • 16 Oct 2020 • Ziye Ma, Somayeh Sojoudi
We analyze the performance of this sequential SDP method both theoretically and empirically, and show that it bridges the gap as the number of cuts increases.
no code implementations • 1 Apr 2020 • Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data.
1 code implementation • 10 Sep 2018 • Matthew Giamou, Ziye Ma, Valentin Peretroukhin, Jonathan Kelly
We present a certifiably globally optimal algorithm for determining the extrinsic calibration between two sensors that are capable of producing independent egomotion estimates.
Robotics