## An Exact Duality Theory for Semidefinite Programming and it's Complexity Implications

### Author: Motakuri Ramana

ABSTRACT

In this paper, we present a new and more complete duality for Semidefinite Programming (SDP), with the following features: \begin{itemize} \item This dual is an explicit semidefinite program, whose number of variables and the coefficient bitlengths are polynomial in those of the primal. \item If the Primal is feasible, then it is bounded if and only if the dual is feasible. \item The duality gap, \ie the difference between the primal and the dual objective function values, is zero whenever the primal is feasible and bounded. Also, in this case, the dual attains its optimum \item It yields a precise Farkas Lemma for semidefinite feasibility systems, \ie characterization of the {\it infeasibility} of a semidefinite inequality in terms of the {\it feasibility} of another polynomial size semidefinite inequality. \end{itemize} Note that the standard duality for Linear Programming satisfies all of the above features, but no such duality theory was previously known for SDP, without Slater-like conditions being assumed. Then we apply the dual to derive certain complexity results for Semidefinite Programming Problems. The decision problem of Semidefinite Feasibility (SDFP), \ie that of determining if a given semidefinite inequality system is feasible, is the central problem of interest. The complexity of SDFP is unknown, but we show the following: 1) In the Turing machine model, SDFP is not NP-Complete unless NP=Co-NP; 2) In the real number model of Blum, Shub and Smale\cite{bss}, SDFP is in NP$\cap$Co-NP. We then give polynomial reductions from the following problems to SDFP: 1) Checking whether an SDP is bounded; 2) Checking whether a feasible and bounded SDP attains the optimum; 3) Checking the optimality of a feasible solution.

Paper available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/1995/95-02.ps.gz