Nonlinear Least Squares

Nonlinear Least Squares is a parameter estimation method that minimizes the sum of squared residuals between observed data and a nonlinear model function, distinguished from linear least squares by its reliance on iterative optimization to solve non-convex, nonlinear dependencies between parameters and predictions.

2.3K

Publications

216.3K

Citations

5.8K

Authors

1.7K

Institutions

Publications per year

2017–2026

391

Variants

Nonlinear Least Squares EstimationNonlinear Least Squares Regression

Authors

5.8K

  1. CP
    C. P. Rinsland

    Langley Research Center, United States

    16 publications · 800 citations

    14

    h-index

  2. DC
    D. Chris Benner

    William & Mary, United States

    13 publications · 753 citations

    13

    h-index

  3. JR
    J. Ross Macdonald

    University of North Carolina at Chapel Hill, United States

    12 publications · 951 citations

    12

    h-index

  4. VM
    V. Malathy Devi

    William & Mary, United States

    12 publications · 711 citations

    12

    h-index

  5. JT
    Joel Tellinghuisen

    Vanderbilt University, United States

    9 publications · 450 citations

    9

    h-index

1–5 of 5.8K

Venues

  1. The Journal of Chemical Physics

    49 publications · 2.8K citations

    29

    h-index

  2. Applied Optics

    42 publications · 2.1K citations

    27

    h-index

  3. IEEE Transactions on Signal Processing

    36 publications · 3.3K citations

    25

    h-index

  4. Journal of Geophysical Research Atmospheres

    JGR · 35 publications · 2.5K citations

    24

    h-index

  5. SIAM Journal on Numerical Analysis

    19 publications · 3K citations

    18

    h-index

1–5