Gradient-square.md

+Gradient square is defined as (in 3d):  
+```math
+\left| \nabla f\right|^2 = \nabla f^* \cdot \nabla f= \left | \frac{\partial f}{\partial x} \right|^2 + \left | \frac{\partial f}{\partial y} \right|^2 + \left | \frac{\partial f}{\partial z} \right|^2
+```
+
+After simple algebra one can show that:  
+```math
+\left| \nabla f\right|^2 = \frac{1}{2}\Delta (f^*\cdot f) - \textrm{Re}\left( f^*\cdot \Delta f \right )
+```
+Numerically, the formula that utilizes laplacians provides results of higher accuracy.  
+
+References:  
+* S. Jin, A. Bulgac, K. Roche, G. Wlazłowski, Coordinate-Space Solver for Superfluid Many-Fermion Systems with Shifted Conjugate Orthogonal Conjugate Gradient Method, [Phys. Rev. C 95, 044302, 2017](https://journals.aps.org/prc/abstract/10.1103/PhysRevC.95.044302), [arXiv:1608.03711](https://arxiv.org/abs/1608.03711)
+* Yue Shi, Precision of finite-difference representation in 3D coordinate-space Hartree-Fock-Bogoliubov calculations, [Phys. Rev. C 98, 014329, 2018](https://journals.aps.org/prc/abstract/10.1103/PhysRevC.98.014329), [arXiv:1805.07919](https://arxiv.org/abs/1805.07919)