leastSquares

leastSquares(spec?): any

Defined in: leastsq.js:165

Minimize 0.5 * sum(r(p)^2) with Levenberg-Marquardt.

Parameters

spec?

bounds?

number[][]

Per-parameter [lo, hi] box bounds (MINUIT transform; null/±Infinity for unbounded sides)

fScale?

number

Residual scale at which the robust losses start to flatten (scipy’s f_scale)

fTol?

number

Relative cost reduction on an accepted step

gTol?

number

Inf-norm of the gradient J^T r

history?

boolean

Record {cost, lambda} per accepted iteration

jacobian?

Function

(p) => m-by-n Array<Array>; central finite differences on the residuals otherwise

lambda0?

number

Initial damping

lambdaDown?

number

Damping decrease factor on acceptance

lambdaUp?

number

Damping increase factor on rejection

loss?

string

‘linear’ | ‘huber’ | ‘soft_l1’ | ‘cauchy’; robust losses down-weight outliers (IRLS, scipy least_squares semantics)

maxIter?

number

Maximum accepted iterations

residuals

Function

(p) => Array of length m

x0

number[]

Initial parameters of length n

xTol?

number

Max relative step component on an accepted step

Returns

any

{x, fx, residuals, iterations, fevals, converged, history?}