Bipartite sign rounding bound

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.