Some optimal choices for a parameter of the Dai–Liao conjugate gradient method are proposed by conducting matrix analyses of the method. More precisely, first the $\ell _{1}$ and $\ell _{\infty }$ norm condition numbers of the search direction matrix are minimized, yielding two adaptive choices for the Dai–Liao parameter. Then we show that a recent formula for computing this parameter which guarantees the descent property can be considered as a minimizer of the spectral condition number as well as the well-known measure function for a symmetrized version of the search direction matrix. Brief convergence analyses are also carried out. Finally, some numerical experiments on a set of test problems related to constrained and unconstrained testing environment, are conducted using a well-known performance profile.

In this paper, we examine the influence of approximate first and/orsecond derivatives on the filter-trust-region algorithm designed forsolving unconstrained nonlinear optimization problems and proposed byGould, Sainvitu and Toint in [12]. Numericalexperiments carried out on small-scaled unconstrained problems fromthe CUTEr collection describe the effect of the use ofapproximate derivatives on the robustness and the efficiency of thefilter-trust-region method.

In this paper, we propose a new class of adaptive trust region methods for unconstrained optimization problems and develop some convergence properties. In the new algorithms, we use the current iterative information to define a suitable initial trust region radius at each iteration. The initial trust region radius is more reasonable in the sense that the trust region model and theobjective function are more consistent at the current iterate. The global convergence, super-linear and quadratic convergence rate are analyzed under some mild conditions. Numericalresults show that some special adaptive trust region methods are available and efficient in practical computation.

An efficient calibration-free visual servoing method is presented. The nonlinear optimization problem is formulated with the residual term for tracking a target. The residual term is estimated based on the secant approximation method. The image Jacobian is estimated using the affine model of the end-effector feature. Some experimental results are presented for a moving target.