tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.
*   Added gradients for `matrix_solve_ls` and `self_adjoint_eig`.
*   Large cleanup to add second order gradient for ops with C++ gradients and
    improve existing gradients such that most ops can now be differentiated
    multiple times.
*   Added a solver for ordinary differential equations,

func(t *testing.T) { testRandomPoint(t, P256()) }) t.Run("P-384", func(t *testing.T) { testRandomPoint(t, P384()) }) t.Run("P-521", func(t *testing.T) { testRandomPoint(t, P521()) }) } func testRandomPoint[P Point[P]](t *testing.T, c *Curve[P]) { t.Cleanup(func() { testingOnlyRejection = nil }) var loopCount int testingOnlyRejection = func() { loopCount++ } // A sequence of all ones will generate 2^N-1, which should be rejected. // (Unless, for example, we are masking too many bits.) r := io.MultiR...

func(t *testing.T) { testRandomPoint(t, P256()) }) t.Run("P-384", func(t *testing.T) { testRandomPoint(t, P384()) }) t.Run("P-521", func(t *testing.T) { testRandomPoint(t, P521()) }) } func testRandomPoint[P Point[P]](t *testing.T, c *Curve[P]) { t.Cleanup(func() { testingOnlyRejection = nil }) var loopCount int testingOnlyRejection = func() { loopCount++ } // A sequence of all ones will generate 2^N-1, which should be rejected. // (Unless, for example, we are masking too many bits.) r := io.MultiR...