Global Convergence Analysis

Global Convergence Analysis is a theoretical framework in mathematical optimization and numerical analysis that characterizes the conditions under which an iterative algorithm is guaranteed to converge to a globally optimal solution or a global root, regardless of the initial starting point.

2.2K

Publications

136.9K

Citations

3.8K

Authors

1.3K

Institutions

Publications per year

2017–2026

632

Authors

3.8K

  1. PK
    Poom Kumam

    King Mongkut's University of Technology Thonburi, Thailand

    25 publications · 749 citations

    19

    h-index

  2. AB
    Auwal Bala Abubakar

    Bayero University Kano, Nigeria

    22 publications · 616 citations

    18

    h-index

  3. PL
    Philippe L. Toint

    University of Namur, Belgium

    15 publications · 2.8K citations

    15

    h-index

  4. LQ
    Liqun Qi

    Hong Kong Polytechnic University, Hong Kong SAR China

    18 publications · 1.9K citations

    15

    h-index

  5. MF
    Masao Fukushima

    Kyoto University, Japan

    15 publications · 1.6K citations

    14

    h-index

1–5 of 3.8K

Venues

  1. SIAM Journal on Optimization

    146 publications · 19K citations

    64

    h-index

  2. IEEE Transactions on Automatic Control

    75 publications · 7.9K citations

    38

    h-index

  3. SIAM Journal on Numerical Analysis

    51 publications · 5.2K citations

    35

    h-index

  4. arXiv (Cornell University)

    Repository · 125 publications · 5.1K citations

    34

    h-index

  5. Mathematical Programming

    40 publications · 3K citations

    31

    h-index

1–5