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For bipartite binary quadratic optimization, if $v^{\ast }$ is the sign optimum and $p_{\mathrm{SDP}}^{\ast }$ its semidefinite relaxation of binary quadratic optimization value, then $c_K{p}_{\mathrm{SDP}}^{\ast }\le v^{\ast }\le p_{\mathrm{SDP}}^{\ast }$. The upper bound follows from rank-one lifting. For the lower bound, Krivine rounding scheme preprocessing followed by Gaussian hyperplane rounding has expected objective exactly $c_K{p}_{\mathrm{SDP}}^{\ast }$, and every rounded vector is feasible. Some outcome reaches at least this expectation. No independence of rounded coordinates or positive semidefiniteness of the original bipartite objective is needed.

Split the sign vector as $x=(p^T,q^T{)}^T$. Multiplication of the two off-diagonal blocks givesbecause the two terms are the same real scalar. The map $(p,q)\mapsto x$ is a bijection between the feasible sign choices. ThereforeThis is the symmetric lifting of bipartite binary quadratic optimization to binary quadratic optimization. The factor $1/2$ is necessary: otherwise the two blocks would double the objective. No positive semidefiniteness of $A$ is assumed; generally its off-diagonal structure makes its quadratic form indefinite.