Alireza Ataei

In this paper, we try to introduce an interior point constraint generation (IPCG) algorithm for semi-infinite linear optimization that has a faster convergence than other algorithms. We will convert the semi-infinite optimization problem to the finite linear optimization problem using discretization, and we will convert the linear problem to the nonlinear problem using the barrier function. In each iteration, we find a point near the central path. We identify the finite number of the constraints violated at the mentioned point from the feasibility set of the semi-infinite problem. Weupdate the feasible region and the barrier parameter simultaneously. We are improving the feasible point for the new feasible region and updating the central path; then, we use the Newton method to find a point nearthe new central path. We continue this process until the barrier parameter reaches the desired accuracy. Numerical results show that this algorithm has better speed and accuracy than others