We define the initial configuration of the articulated chain, the objective end-effector, and a squared object.
Antoniewiecz et al. approached the problem of defining confidence regions for flux estimates by minimizing a sum of squared residuals objective function as a function of the flux value [ 17].
The chi-square objective function was minimized to adjust model parameters.
The operator equation is reduced to a minimization problem via a least-squares objective functional.
The advantage of RIFT over the commonly used least-squares objective function is demonstrated.
The Laplace integral in the flotation kinetic equation is approximated by the Lobatto quadrature formula and the cumulative distribution is evaluated by optimizing a least square objective function.
In this paper the shape gradient of the least-squares objective functional is derived with the help of the shape sensitivity analysis and adjoint problem method.
We also formulate the optimal weighting to be used in the least-squares objective in the covariance estimation problem to ensure minimum variance in the estimates.
The design is formulated as a multi-constrained optimization problem with a least square objective function to minimize structural shape errors.
We present a theoretical analysis of the way a Least-Square objective function and a Levenberg Marquardt minimization algorithm are affected by the introduction of reciprocal information in the inverse problem.
The parameter identification is performed by the minimization of the ordinary least squares objective function comparing the measured temperatures to the response of a direct model function of thermophysical parameters.
