  The development of nonlinear optimization algorithms capable of performing
reliably in the presence of noise has garnered considerable attention lately.
This paper advocates for strategies to create noise-tolerant nonlinear
optimization algorithms by adapting classical deterministic methods. These
adaptations follow certain design guidelines described here, which make use of
estimates of the noise level in the problem. The application of our methodology
is illustrated by the development of a line search gradient projection method,
which is tested on an engineering design problem. It is shown that a new
self-calibrated line search and noise-aware finite-difference techniques are
effective even in the high noise regime. Numerical experiments investigate the
resiliency of key algorithmic components. A convergence analysis of the line
search gradient projection method establishes convergence to a neighborhood of
the solution.
