Summary: We compare the performance of several robust large-scale minimization algorithms for the unconstrained minimization of an ill-posed inverse problem. The parabolized Navier-Stokes equation model was used for adjoint parameter estimation. The methods compared consist of three versions of nonlinear conjugate-gradient (CG) method, quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS), the limited-memory quasi-Newton (L-BFGS) [

*D. C. Liu* and ıJ. Nocedal, Math. Program., Ser. B 45, No. 3, 503–528 (1989;

Zbl 0696.90048)], truncated Newton (T-N) method [

*S. G. Nash*, SIAM J. Sci. Stat. Comput. 6, 599–616 (1985); SIAM J. Numer. Anal. 21, 770–788 (1984;

Zbl 0558.65041)] and a new hybrid algorithm proposed by

*J. L. Morales* and

*J. Nocedal* [Comput. Optim. Appl. 21, No. 2, 143–154 (2002;

Zbl 0988.90035)]. For all the methods employed and tested, the gradient of the cost function is obtained via an adjoint method. A detailed description of the algorithmic form of minimization algorithms employed in the minimization comparison is provided. For the inviscid case, the CG-descent method of Hager [

*W. W. Hager* and

*H. Zhang*, SIAM J. Optim. 16, No. 1, 170–192 (2005;

Zbl 1093.90085)] performed the best followed closely by the hybrid method [Morales and Nocedal, loc. cit.], while in the viscous case, the hybrid method emerged as the best performed followed by CG [

*D. F. Shanno* and

*K. H. Phua*, ACM Trans. Math. Softw. 15, No. 4, 618–622 (1989)] and CG-descent [

*W. W. Hager* and

*H. Zhang*, SIAM J. Optim. 16, No. 1, 170–192 (2005;

Zbl 1093.90085)]. This required an adequate choice of parameters in the CG-descent method as well as controlling the number of L-BFGS and T-N iterations to be interlaced in the hybrid method.