<p>In this paper, a line-search filter adaptive regularization algorithm using cubics based on affine scaling interior-point methods is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables. In each iteration, an appropriate affine scaling matrix is introduced to construct an affine scaling subproblem with linearization constraints based on the optimality conditions of the problem. This coincides with sequential quadratic optimization methods in spirit. To compute the search direction, composite step methods and reduced Hessian methods are applied to handle the linearized constraints. Then we obtain a standard unconstrained ARC subproblem and its (approximate) solution can supply sufficient decrease to guarantee the convergence. The fraction to the boundary rule is used to ensure the strict feasibility (for nonnegative constraints on variables) of every iteration point. After obtaining the search direction, the line-search filter technique is used to determine the new iteration point. The regularization parameters are updated based on the ratio of the objective function’s descent to the model’s descent. Under appropriate assumptions, the global convergence of the algorithm is proved, and preliminary numerical results are presented.</p>