We introduce a first-order method for solving very large convex cone programs. Themethod uses an operator splittingmethod, the alternating directionsmethod of multipliers, to solve the homogeneous self-dual embedding, an equivalent feasibility problem involving finding a nonzero point in the intersection of a subspace and a cone. This approach has several favorable properties. Compared to inter...

We discuss several different search directions which can be used in primal-dual interior-point methods for semidefinite programming problems and investigate their theoretical properties, including scale invariance, primal-dual symmetry, and whether they always generate well-defined directions. Among the directions satisfying all but at most two of these desirable properties are the Alizadeh-Hae...

Interior point methods for nonlinear programs (NLP) are adapted for solution of mathematical programs with complementarity constraints (MPCCs). The constraints of the MPCC are suitably relaxed so as to guarantee a strictly feasible interior for the inequality constraints. The standard primal-dual algorithm has been adapted with a modified step calculation. The algorithm is shown to be superline...

Abstract A new primal-dual interior-point algorithm applicable to nonsymmetric conic optimization is proposed. It a generalization of the famous suggested by Nesterov and Todd for symmetric case, uses scalings cones proposed Tunçel. We specialize Tunçel’s important case 3 dimensional exponential-cones, resulting in practical with good numerical performance, on level standard cone ( e.g. , quadr...