An interior-point $\ell_1$-penalty method for nonlinear optimization Nick Gould (RAL) joint with Dominique Orban (Montr\'{e}al) and Philippe Toint (Namur) We discuss the merits of a mixed interior/exterior-point method for nonlinear programming in which all nonlinear constraints are treated by an $\ell_{1}$ penalty function. Building on a proposal by Mayne and Polak (1976), a suitable decomposition of the constraints allows us to derive an exact differentiable penalty function involving only inequality constraints, which may then be treated using a logarithmic barrier. Exactness of the exterior penalty function eliminates the need to drive the corresponding penalty parameter to infinity. Global and fast local convergence of the proposed scheme are exposed. A special purpose trust-rgeion method for the underlying penalty-barrier subproblem will be outlined. The algorithm is implemented as part of the GALAHAD library under the same SUPERB. Numerical results comparing the present method to state-of-the-art nonlinear programming codes will be reported.