Symmetric Grothendieck inequality

We establish an analogue of the Grothendieck inequality where the rectangular matrix is replaced by a symmetric/Hermitian matrix and the bilinear form by a quadratic form. We call this the symmetric Grothendieck inequality; despite its name, it is a generalization -- the original Grothendieck inequality is a special case. While there are other proposals for such an inequality, ours differs in two important ways: (i) we have no additional requirement like positive semidefiniteness; (ii) our symmetric Grothendieck constant is universal, i.e., independent of the matrix and its dimensions. A consequence of our symmetric Grothendieck inequality is a "conic Grothendieck inequality" for any family of cones of symmetric matrices: The original Grothendieck inequality is a special case; as is the Nesterov $\pi/2$-Theorem, which corresponds to the cones of positive semidefinite matrices; as well as the Goemans-Williamson inequality, which corresponds to the cones of Laplacians. For yet other cones, e.g., of diagonally dominant matrices, we get new Grothendieck-like inequalities. A slight extension leads to a unified framework that treats any Grothendieck-like inequality as an inequality between two norms within a family of "Grothendieck norms" restricted to a family of cones. This allows us to place on equal footing the Goemans-Williamson inequality, Nesterov $\pi/2$-Theorem, Ben-Tal-Nemirovski-Roos $4/\pi$-Theorem, generalized Grothendieck inequality, order-$p$ Grothendieck inequality, rank-constrained positive semidefinite Grothendieck inequality; and in turn allows us to simplify proofs, extend results from real to complex, obtain new bounds or establish sharpness of existing ones. The symmetric Grothendieck inequality may also be applied to obtain polynomial-time approximation bounds for NP-hard combinatorial, integer, and nonconvex optimization problems.