# Non-linear least squares

nonlinear least squaresNLLSnon-linear least-squares estimationnon-linear systemsnonlinearnonlinear least-squares fittingNumerical methods for non-linear least squares
Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n).wikipedia
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### Least squares

least-squaresmethod of least squaresleast squares method
Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n). There are many similarities to linear least squares, but also some significant differences.
Least-squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the residuals are linear in all unknowns.

### Gauss–Newton algorithm

Gauss-NewtonGauss–NewtonGauss–Newton method
These equations form the basis for the Gauss–Newton algorithm for a non-linear least squares problem.
The Gauss–Newton algorithm is used to solve non-linear least squares problems.

### Linear least squares

normal equationslinear least-squaresLinear least squares (mathematics)
There are many similarities to linear least squares, but also some significant differences.
In contrast, non-linear least squares problems generally must be solved by an iterative procedure, and the problems can be non-convex with multiple optima for the objective function.

### Nonlinear regression

non-linear regressionnonlinearnon-linear
It is used in some forms of nonlinear regression.
For details concerning nonlinear data modeling see least squares and non-linear least squares.

### Levenberg–Marquardt algorithm

Levenberg-Marquardt algorithmLevenberg–MarquardtLevenberg-Marquardt
This can be achieved by using the Marquardt parameter.
In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems.

### Jacobian matrix and determinant

Jacobian matrixJacobianJacobian determinant
:The Jacobian, J, is a function of constants, the independent variable and the parameters, so it changes from one iteration to the next.
The Jacobian serves as a linearized design matrix in statistical regression and curve fitting; see non-linear least squares.

### Grey box model

Grey box completion and validationgrey-box
Once a selection of non-zero values is made, the remaining coefficients in A can be determined by minimizing m(f,p,Ac) over the data with respect to the nonzero values in A, typically by non-linear least squares.

### Nonlinear programming

non-linear programmingnonlinear programnonlinear optimization

### Errors and residuals

residualserror termresidual
:is minimized, where the residuals (in-sample prediction errors) r i are given by :

### Maxima and minima

maximumminimumlocal maximum
The minimum value of S occurs when the gradient is zero.

The minimum value of S occurs when the gradient is zero.

### Taylor series

Taylor expansionMaclaurin seriesTaylor polynomial
At each iteration the model is linearized by approximation to a first-order Taylor polynomial expansion about

### Diagonal matrix

diagonaldiagonal matricesscalar matrices
Each element of the diagonal weight matrix W should, ideally, be equal to the reciprocal of the error variance of the measurement.

### Variance

sample variancepopulation variancevariability
Each element of the diagonal weight matrix W should, ideally, be equal to the reciprocal of the error variance of the measurement.

### Mathematical optimization

optimizationmathematical programmingoptimal
In linear least squares the objective function, S, is a quadratic function of the parameters.

In linear least squares the objective function, S, is a quadratic function of the parameters.

### Parabola

parabolicparabolic curveparabolic arc
:When there is only one parameter the graph of S with respect to that parameter will be a parabola.

### Ellipse

ellipticalellipticeccentricity
With two or more parameters the contours of S with respect to any pair of parameters will be concentric ellipses (assuming that the normal equations matrix is positive definite).

### Definiteness of a matrix

positive definitepositive semidefinitepositive-definite
With two or more parameters the contours of S with respect to any pair of parameters will be concentric ellipses (assuming that the normal equations matrix is positive definite).

### Computer simulation

computer modelsimulationcomputer modeling
A good way to do this is by computer simulation.

### Round-off error

rounding errorrounding errorsroundoff error
The increment, size should be chosen so the numerical derivative is not subject to approximation error by being too large, or round-off error by being too small.

### Cauchy distribution

LorentzianCauchyLorentzian distribution

### Semi-log plot

log-linearSemi-log graphSemilog graph
:Graphically this corresponds to working on a semi-log plot.

### Log-normal distribution

lognormallog-normallognormal distribution
:This procedure should be avoided unless the errors are multiplicative and log-normally distributed because it can give misleading results.

### Lineweaver–Burk plot

Lineweaver-Burk plotLineweaver-Burke-diagramKm
:.The Lineweaver–Burk plot