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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.

Applying a scalar function separately to each matrix entry: $(f[X]{)}_{ij}=f(X_{ij})$. This differs from spectral matrix functional calculus. In particular, $\arcsin [Y]$ in Gaussian hyperplane rounding is entrywise, and its contribution to a matrix trace can be restricted to selected blocks by the support of the other matrix.

For a bipartite elliptope matrix $X$, put $t=\log (1+\sqrt{2})$ and apply $\sinh (tx)$ within the two diagonal blocks and $\sin (tx)$ across them. Matching absolute power series coefficients give a positive semidefinite matrix by coefficient-dominated entrywise positivity, while $\sinh t=1$ gives unit diagonal. Gaussian hyperplane rounding then turns each cross-block correlation coefficient into $(2/\pi )\arcsin (\sin (t{X}_{ij}))=(2t/\pi )X_{ij}$ because $t<\pi /2$. The zero diagonal blocks of the bipartite objective eliminate every other contribution.

The Gram matrix representation and $Y_{ii}=1$ give $\Vert v_i{\Vert }_2=1$. For a standard Gaussian random vector $Z$, each $⟨v_i,Z⟩$ is a standard normal scalar, so the zero event has probability zero. Choose either sign convention at zero; the resulting vector is almost surely a feasible sign vector. Consequently $y^{T}Ay\le v^{\ast }$ almost surely.

The Gaussian sign-correlation identity givesThe last equality uses symmetry of the real matrices. HenceThe inverse sine is applied entrywise, not through spectral matrix functional calculus.

For completeness, the correlation coefficient identity has a geometric proof. If the angle between two unit vectors is $\theta$, the isotropic Gaussian random vector direction in their two-dimensional span gives opposite signs on angular sectors with probability $\theta /\pi$. Thus the sign product has expectation $1-2\theta /\pi =(2/\pi )\arcsin (\cos \theta )$. Parallel and antiparallel pairs give the endpoint values $1$ and $-1$ directly. This is Gaussian hyperplane rounding, and needs no independence between the rounded coordinates.

A method turning a solution of a continuous optimization relaxation into a random feasible solution of a discrete problem. An expected objective bound can give an approximation guarantee. Gaussian hyperplane rounding and Rademacher rounding for a semidefinite relaxation are different examples.