diff --git a/quantecon/optimize/__init__.py b/quantecon/optimize/__init__.py
index 4937fb738..ad3267faf 100644
--- a/quantecon/optimize/__init__.py
+++ b/quantecon/optimize/__init__.py
@@ -3,4 +3,4 @@
 """
 
 from .scalar_maximization import brent_max
-
+from .root_finding import newton, newton_halley, newton_secant
diff --git a/quantecon/optimize/root_finding.py b/quantecon/optimize/root_finding.py
new file mode 100644
index 000000000..e88e7de51
--- /dev/null
+++ b/quantecon/optimize/root_finding.py
@@ -0,0 +1,257 @@
+import numpy as np
+from numba import jit, njit
+from collections import namedtuple
+
+__all__ = ['newton', 'newton_halley', 'newton_secant']
+
+_ECONVERGED = 0
+_ECONVERR = -1
+
+results = namedtuple('results', 
+                      ('root function_calls iterations converged'))
+
+@njit
+def _results(r):
+    r"""Select from a tuple of(root, funccalls, iterations, flag)"""
+    x, funcalls, iterations, flag = r
+    return results(x, funcalls, iterations, flag == 0)
+
+@njit
+def newton(func, x0, fprime, args=(), tol=1.48e-8, maxiter=50,
+           disp=True):
+    """
+    Find a zero from the Newton-Raphson method using the jitted version of 
+    Scipy's newton for scalars. Note that this does not provide an alternative 
+    method such as secant. Thus, it is important that `fprime` can be provided.
+
+    Note that `func` and `fprime` must be jitted via Numba.
+    They are recommended to be `njit` for performance.
+
+    Parameters
+    ----------
+    func : callable and jitted
+        The function whose zero is wanted. It must be a function of a
+        single variable of the form f(x,a,b,c...), where a,b,c... are extra
+        arguments that can be passed in the `args` parameter.
+    x0 : float
+        An initial estimate of the zero that should be somewhere near the
+        actual zero.
+    fprime : callable and jitted
+        The derivative of the function (when available and convenient).
+    args : tuple, optional
+        Extra arguments to be used in the function call.
+    tol : float, optional
+        The allowable error of the zero value.
+    maxiter : int, optional
+        Maximum number of iterations.
+    disp : bool, optional
+        If True, raise a RuntimeError if the algorithm didn't converge
+
+    Returns
+    -------
+    results : namedtuple
+        root - Estimated location where function is zero.
+        function_calls - Number of times the function was called.
+        iterations - Number of iterations needed to find the root.
+        converged - True if the routine converged
+    """
+
+    if tol <= 0:
+        raise ValueError("tol is too small <= 0")
+    if maxiter < 1:
+        raise ValueError("maxiter must be greater than 0")
+
+    # Convert to float (don't use float(x0); this works also for complex x0)
+    p0 = 1.0 * x0
+    funcalls = 0
+    status = _ECONVERR
+
+    # Newton-Raphson method
+    for itr in range(maxiter):
+        # first evaluate fval
+        fval = func(p0, *args)
+        funcalls += 1
+        # If fval is 0, a root has been found, then terminate
+        if fval == 0:
+            status = _ECONVERGED
+            p = p0
+            itr -= 1
+            break
+        fder = fprime(p0, *args)
+        funcalls += 1
+        # derivative is zero, not converged
+        if fder == 0:
+            p = p0
+            break
+        newton_step = fval / fder
+        # Newton step
+        p = p0 - newton_step   
+        if abs(p - p0) < tol:
+            status = _ECONVERGED
+            break
+        p0 = p
+    
+    if disp and status == _ECONVERR:
+        msg = "Failed to converge"
+        raise RuntimeError(msg)
+
+    return _results((p, funcalls, itr + 1, status))
+
+@njit
+def newton_halley(func, x0, fprime, fprime2, args=(), tol=1.48e-8,
+                  maxiter=50, disp=True):
+    """
+    Find a zero from Halley's method using the jitted version of
+    Scipy's.
+
+    `func`, `fprime`, `fprime2` must be jitted via Numba.
+
+    Parameters
+    ----------
+    func : callable and jitted
+        The function whose zero is wanted. It must be a function of a
+        single variable of the form f(x,a,b,c...), where a,b,c... are extra
+        arguments that can be passed in the `args` parameter.
+    x0 : float
+        An initial estimate of the zero that should be somewhere near the
+        actual zero.
+    fprime : callable and jitted
+        The derivative of the function (when available and convenient).
+    fprime2 : callable and jitted
+        The second order derivative of the function
+    args : tuple, optional
+        Extra arguments to be used in the function call.
+    tol : float, optional
+        The allowable error of the zero value.
+    maxiter : int, optional
+        Maximum number of iterations.
+    disp : bool, optional
+        If True, raise a RuntimeError if the algorithm didn't converge
+
+    Returns
+    -------
+    results : namedtuple
+        root - Estimated location where function is zero.
+        function_calls - Number of times the function was called.
+        iterations - Number of iterations needed to find the root.
+        converged - True if the routine converged
+    """
+
+    if tol <= 0:
+        raise ValueError("tol is too small <= 0")
+    if maxiter < 1:
+        raise ValueError("maxiter must be greater than 0")
+
+    # Convert to float (don't use float(x0); this works also for complex x0)
+    p0 = 1.0 * x0
+    funcalls = 0
+    status = _ECONVERR
+
+    # Halley Method
+    for itr in range(maxiter):
+        # first evaluate fval
+        fval = func(p0, *args)
+        funcalls += 1
+        # If fval is 0, a root has been found, then terminate
+        if fval == 0:
+            status = _ECONVERGED
+            p = p0
+            itr -= 1
+            break
+        fder = fprime(p0, *args)
+        funcalls += 1
+        # derivative is zero, not converged
+        if fder == 0:
+            p = p0
+            break
+        newton_step = fval / fder
+        # Halley's variant
+        fder2 = fprime2(p0, *args)
+        p = p0 - newton_step / (1.0 - 0.5 * newton_step * fder2 / fder)
+        if abs(p - p0) < tol:
+            status = _ECONVERGED
+            break
+        p0 = p
+
+    if disp and status == _ECONVERR:
+        msg = "Failed to converge"
+        raise RuntimeError(msg)
+
+    return _results((p, funcalls, itr + 1, status))
+
+@njit
+def newton_secant(func, x0, args=(), tol=1.48e-8, maxiter=50,
+                  disp=True):
+    """
+    Find a zero from the secant method using the jitted version of 
+    Scipy's secant method.
+    
+    Note that `func` must be jitted via Numba.
+    
+    Parameters
+    ----------
+    func : callable and jitted
+        The function whose zero is wanted. It must be a function of a
+        single variable of the form f(x,a,b,c...), where a,b,c... are extra
+        arguments that can be passed in the `args` parameter.
+    x0 : float
+        An initial estimate of the zero that should be somewhere near the
+        actual zero.
+    args : tuple, optional
+        Extra arguments to be used in the function call.
+    tol : float, optional
+        The allowable error of the zero value.
+    maxiter : int, optional
+        Maximum number of iterations.
+    disp : bool, optional
+        If True, raise a RuntimeError if the algorithm didn't converge.
+
+    Returns
+    -------
+    results : namedtuple
+        root - Estimated location where function is zero.
+        function_calls - Number of times the function was called.
+        iterations - Number of iterations needed to find the root.
+        converged - True if the routine converged
+    """
+    
+    if tol <= 0:
+        raise ValueError("tol is too small <= 0")
+    if maxiter < 1:
+        raise ValueError("maxiter must be greater than 0")
+    
+    # Convert to float (don't use float(x0); this works also for complex x0)
+    p0 = 1.0 * x0
+    funcalls = 0
+    status = _ECONVERR
+    
+    # Secant method
+    if x0 >= 0:
+        p1 = x0 * (1 + 1e-4) + 1e-4
+    else:
+        p1 = x0 * (1 + 1e-4) - 1e-4
+        q0 = func(p0, *args)
+    funcalls += 1
+    q1 = func(p1, *args)
+    funcalls += 1
+    for itr in range(maxiter):
+        if q1 == q0:
+            p = (p1 + p0) / 2.0
+            status = _ECONVERGED
+            break
+        else:
+            p = p1 - q1 * (p1 - p0) / (q1 - q0)
+        if np.abs(p - p1) < tol:
+            status = _ECONVERGED
+            break
+        p0 = p1
+        q0 = q1
+        p1 = p
+        q1 = func(p1, *args)
+        funcalls += 1
+        
+    if disp and status == _ECONVERR:
+        msg = "Failed to converge"
+        raise RuntimeError(msg)
+
+    return _results((p, funcalls, itr + 1, status))
\ No newline at end of file
diff --git a/quantecon/optimize/tests/test_root_finding.py b/quantecon/optimize/tests/test_root_finding.py
new file mode 100644
index 000000000..ad0015ce4
--- /dev/null
+++ b/quantecon/optimize/tests/test_root_finding.py
@@ -0,0 +1,123 @@
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_allclose
+from numba import njit
+
+from quantecon.optimize import newton, newton_halley, newton_secant
+
+@njit
+def func(x):
+    """
+    Function for testing on.
+    """
+    return (x**3 - 1)
+
+
+@njit
+def func_prime(x):
+    """
+    Derivative for func.
+    """
+    return (3*x**2)
+
+@njit
+def func_prime2(x):
+    """
+    Second order derivative for func.
+    """
+    return 6*x
+
+@njit
+def func_two(x):
+    """
+    Harder function for testing on.
+    """
+    return np.sin(4 * (x - 1/4)) + x + x**20 - 1
+
+
+@njit
+def func_two_prime(x):
+    """
+    Derivative for func_two.
+    """
+    return 4*np.cos(4*(x - 1/4)) + 20*x**19 + 1
+
+@njit
+def func_two_prime2(x):
+    """
+    Second order derivative for func_two
+    """
+    return 380*x**18 - 16*np.sin(4*(x - 1/4))
+
+
+def test_newton_basic():
+    """
+    Uses the function f defined above to test the scalar maximization 
+    routine.
+    """
+    true_fval = 1.0
+    fval = newton(func, 5, func_prime)
+    assert_almost_equal(true_fval, fval.root, decimal=4)
+    
+
+def test_newton_basic_two():
+    """
+    Uses the function f defined above to test the scalar maximization 
+    routine.
+    """
+    true_fval = 1.0
+    fval = newton(func, 5, func_prime)
+    assert_allclose(true_fval, fval.root, rtol=1e-5, atol=0)
+    
+    
+def test_newton_hard():
+    """
+    Harder test for convergence.
+    """
+    true_fval = 0.408
+    fval = newton(func_two, 0.4, func_two_prime)
+    assert_allclose(true_fval, fval.root, rtol=1e-5, atol=0.01)
+    
+def test_halley_basic():
+    """
+    Basic test for halley method
+    """
+    true_fval = 1.0
+    fval = newton_halley(func, 5, func_prime, func_prime2)
+    assert_almost_equal(true_fval, fval.root, decimal=4)
+
+def test_halley_hard():
+    """
+    Harder test for halley method
+    """
+    true_fval = 0.408
+    fval = newton_halley(func_two, 0.4, func_two_prime, func_two_prime2)
+    assert_allclose(true_fval, fval.root, rtol=1e-5, atol=0.01)
+
+def test_secant_basic():
+    """
+    Basic test for secant option.
+    """
+    true_fval = 1.0
+    fval = newton_secant(func, 5)
+    assert_allclose(true_fval, fval.root, rtol=1e-5, atol=0.001)
+
+
+def test_secant_hard():
+    """
+    Harder test for convergence for secant function.
+    """
+    true_fval = 0.408
+    fval = newton_secant(func_two, 0.4)
+    assert_allclose(true_fval, fval.root, rtol=1e-5, atol=0.01)
+
+
+# executing testcases.
+
+if __name__ == '__main__':
+    import sys
+    import nose
+
+    argv = sys.argv[:]
+    argv.append('--verbose')
+    argv.append('--nocapture')
+    nose.main(argv=argv, defaultTest=__file__)