We present a Markov Chain, “Dikin walk”, for sampling from a convex body equipped with a self-concordant barrier. This Markov Chain corresponds to a natural random walk with respect to a Riemannian metric defined using the Hessian of the barrier function. For every convex set of dimension n, there exists a self-concordant barrier whose self-concordance parameter is O(n). Consequently, a rapidly...

In this paper we take up once again the subject of the geometry of the central paths of linear programming theory. We study the boundary behavior of these paths as in Meggido and Shub [5], but from a different perspective and with a different emphasis. Our main goal will be to give a global picture of the central paths even for degenerate problems as solution curves of the Newton vector field, ...

We develop and compare multilevel algorithms for solving constrained nonlinear variational problems via interior point methods. Several equivalent formulations of the linear systems arising at each iteration of the interior point method are compared from the point of view of conditioning and iterative solution. Furthermore, we show how a multilevel continuation strategy can be used to obtain go...

We establish polynomial-time convergence of infeasible-interior-point methods for conic programs over symmetric cones using a wide neighborhood of the central path. The convergence is shown for a commutative family of search directions used in Schmieta and Alizadeh [9]. These conic programs include linear and semidefinite programs. This extends the work of Rangarajan and Todd [8], which establi...

We present a structured interior-point method for the efficient solution of the optimal control problem in model predictive control. The cost of this approach is linear in the horizon length, compared with cubic growth for a naive approach. We use a discrete-time Riccati recursion to solve the linear equations efficiently at each iteration of the interior-point method, and show that this recurs...

In this paper we develop several polynomial-time interior-point methods (IPM) for solving nonlinear primal-dual conic optimization problem. We assume that the barriers for the primal and the dual cone are not conjugate. This broken symmetry does not allow to apply the standard primal-dual IPM. However, we show that in this situation it is also possible to develop very efficient optimization met...

where b, y ∈ IR; ci, xi, si ∈ IRi , Ai ∈ IRm×ni , i = 1, . . . , r. For each i = 1, . . . , r, xi and si are the primal and dual slack variables associated with the ith cone and K∗ i = { si ∈ IRi : xi si ≥ 0, ∀xi ∈ Ki } (3) is the dual cone to Ki. We assume that Ki,K i , i = 1, . . . , r are pointed closed convex cones with nonempty interiors. Let K = K1 × K2 × . . . × Kr be the overall cone in...

This paper presents a strategy for choosing the initial point slacks and multipliers in interior methods for nonlinear programming It consists of rst computing a Newton like step to estimate the magnitude of these three variables and then shifting the slacks and multipliers so that they are su ciently positive The new strategy has the option of respecting the initial estimate of the solution gi...

3 Solution techniques 13 3.1 Combinatorial approach : : : : : : : : : : : : : : : : : : : : : : : : : : 13 3.2 Continuous approach : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14 3.2.1 Examples of embedding : : : : : : : : : : : : : : : : : : : : : : 14 3.2.2 Global approximation : : : : : : : : : : : : : : : : : : : : : : : 15 3.2.3 Continuous trajectories : : : : : : : : : : : : : ...