  Packing and covering semidefinite programs (SDPs) appear in natural
relaxations of many combinatorial optimization problems as well as a number of
other applications. Recently, several techniques were proposed, that utilize
the particular structure of this class of problems, to obtain more efficient
algorithms than those offered by general SDP solvers. For certain applications,
such as those described in this paper, it may be desirable to obtain {\it
sparse} dual solutions, i.e., those with support size (almost) independent of
the number of primal constraints. In this paper, we give an algorithm that
finds such solutions, which is an extension of a {\it logarithmic-potential}
based algorithm of Grigoriadis, Khachiyan, Porkolab and Villavicencio (SIAM
Journal of Optimization 41 (2001)) for packing/covering linear programs.
