a1be7bf2f0926707e25b02570c17ba373878f768,pymanopt/manifolds/psd.py,PositiveDefinite,exp,#PositiveDefinite#Any#Any#,110

Before Change

            c_inv = la.inv(c)
            e = multiexp(multiprod(multiprod(c_inv, u), multitransp(c_inv)),
                         sym=True)
            return multiprod(multiprod(c, e), multitransp(c))

    def log(self, x, y):
        c = la.cholesky(x)
        c_inv = la.inv(c)

After Change

    def exp(self, x, u):
        // TODO: Check which method is faster depending on n, k.
        x_inv_u = np.linalg.solve(x, u)
        if self._k > 1:
            e = np.zeros(np.shape(x))
            for i in range(self._k):
                e[i] = sp.linalg.expm(x_inv_u[i])
        else:
            e = sp.linalg.expm(x_inv_u)
        return multiprod(x, e)
        // This alternative implementation is sometimes faster though less
        // stable. It can return a matrix with small negative determinant.
        //    c = la.cholesky(x)
        //    c_inv = la.inv(c)

Instances