مشخصات پژوهش

After a brief introduction to Euclidean Jordan algebra, we present a new corrector–predictor path-following interior-point algorithm for convex, quadratic, and symmetric cone optimization. In each iteration, the algorithm involves two kind of steps: a predictor (affine-scaling) step and a full Nesterov and Todd (centring) step. Moreover, we derive the complexity for the algorithm, and we obtain the best-known iteration bound for the small-update method