MO4.R9.1

On the Success Probability of the $L_0$-regularized Box-constrained Babai Point

Xiao-Wen Chang, Yingzi Xu, McGill University, Canada
Session:
AMP, Sparsity and Sketching
Track:
11: Information Theory and Statistics
Location:
Lamda
Presentation Time:
Mon, 8 Jul, 16:25 - 16:45
Session Chair:
Ramji Venkataramanan, University of Cambridge
Abstract
We consider the success probability of the $L_0$-regularized box-constrained Babai point, which is a suboptimal solution to the $L_0$-regularized box-constrained integer least squares problem and can be used for MIMO detection. First, we derive formulas for the success probability of both $L_0$-regularized and unregularized box-constrained Babai points. Then we investigate the properties of the $L_0$-regularized box-constrained Babai point, including the optimality of the regularization parameter and the monotonicity of the ratio of the two success probabilities. Finally a bound on the success probability of the $L_0$-regularized Babai point is derived.
Session MO4.R9
MO4.R9.1: On the Success Probability of the $L_0$-regularized Box-constrained Babai Point
Xiao-Wen Chang, Yingzi Xu, McGill University, Canada
MO4.R9.2: Linear Operator Approximate Message Passing: Power Method with Partial and Stochastic Updates
Riccardo Rossetti, Duke University, United States; Bobak Nazer, Boston University, United States; Galen Reeves, Duke University, United States
MO4.R9.3: A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification
Burak Çakmak, Technical University of Berlin, Germany; Yue M. Lu, Harvard University, United States; Manfred Opper, Technical University of Berlin, Germany
MO4.R9.4: Efficient Nonconvex Optimization for Two-way Sparse Reduced-Rank Regression
Cheng Cheng, Ziping Zhao, ShanghaiTech University, China
Resources
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