Let be the operator

Then the adjoint of with respect to the standard inner products in and is the operator

Our homogeneous and self-dual linear feasibility model for SDP is based on those appearing in [11] for linear programming (LP). It has the following form:

where are positive semidefinite. A solution to this system with positive either gives optimal solutions to the SDP and its dual or gives a certificate of primal or dual infeasibility.

At each iteration of our algorithms, we apply Newton's method to a perturbation of equation (22), namely,

where the right-hand side quantities are considered as fixed. Here and are parameters, and and are defined in (26) and (25) below, respectively.

Just as in the case of infeasible path-following methods,
the search direction

at each iteration of our homogeneous algorithms
is computed from a symmetrized Newton equation
derived from the perturbed equation (23):

where and , are defined as in (6) and (7), respectively, and

We compute the search direction via a Schur complement equation as follows. First compute from the equation

where

Then compute , , and from the equations