Scaled nonlinear conjugate gradient methods for nonlinear least squares problems
Scaled nonlinear conjugate gradient methods for nonlinear least squares problems is a scholarly work, published in 2018 in ''Numerical Algorithms''. The main subjects of the publication include line search, gradient descent, Descent direction, nonlinear system, conjugate gradient method, convexity, iterative numerical method, rank, curvature, Nonlinear conjugate gradient method, non-linear least squares, least-squares function approximation, mathematical optimization, residual, compressed sensing, applied mathematics, mathematics, Jacobian matrix, and algorithm. The authors propose a modified structured secant relation to get a more accurate approximation of the second curvature of the least squares objective function.