MM optimization algorithms

By K. Lange. 2016

299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/b32eb1aecd6afd3dc6566d9782c9e3db077d2234

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Shan You
3. Chang Xu
4. Chao Xu
5. Juyong Zhang
6. Xiangyu Yang
7. Hao Wang
8. Xiao Wang

In Industrial and Manufacturing Engineering

1. Xiangyu Yang
2. Hao Wang
3. Xiao Wang
4. Yichen Zhu

In Numerical Analysis

1. Dacheng Tao
2. Chang Xu
3. Yuxin Yao
4. Juyong Zhang
5. Xiangyu Yang
6. Hao Wang
7. Xiao Wang
8. Yichen Zhu

Citations in the corpus

• Fast and Robust Iterative Closest Point (2020, background): “Our key observation is that classical ICP is a majorization-minimization (MM) algorithm [7] for minimizing (cid:96) 2 distance between the two point sets, which iteratively constructs and minimizes a surrogate function and ensures monotonic decrease of the target energy.”
• Minimization Over the Nonconvex Sparsity Constraint Using A Hybrid First-order method (2021, methodology): “(i) IRBP is formulated within the MM algorithmic framework [30].”
• Learning With Single-Teacher Multi-Student (2018, background): “Majorization minimization (MM) algorithms substitute a simple optimization problem for a difficult optimization problem (Hunter and Lange 2004; Mairal 2015; Lange 2016).”