From 152c067552ed19cf6379340483822d9a73d8b89c Mon Sep 17 00:00:00 2001
From: John Stachurski <john.stachurski@gmail.com>
Date: Wed, 6 Jun 2018 22:03:48 +1000
Subject: [PATCH 01/12] adding jitted scalar maximization routine, first build

---
 quantecon/__init__.py                       |   1 +
 quantecon/optimize/__init__.py              |   6 +
 quantecon/optimize/scalar_maximization.py   | 135 ++++++++++++++++++++
 quantecon/optimize/tests/test_scalar_max.py |  39 ++++++
 quantecon/version.py                        |   2 +-
 5 files changed, 182 insertions(+), 1 deletion(-)
 create mode 100644 quantecon/optimize/__init__.py
 create mode 100644 quantecon/optimize/scalar_maximization.py
 create mode 100644 quantecon/optimize/tests/test_scalar_max.py

diff --git a/quantecon/__init__.py b/quantecon/__init__.py
index 7600dc6e4..1428a7f35 100644
--- a/quantecon/__init__.py
+++ b/quantecon/__init__.py
@@ -12,6 +12,7 @@
 from . import game_theory
 from . import quad
 from . import random
+from . import optimize
 
 #-Objects-#
 from .compute_fp import compute_fixed_point
diff --git a/quantecon/optimize/__init__.py b/quantecon/optimize/__init__.py
new file mode 100644
index 000000000..ea25f0199
--- /dev/null
+++ b/quantecon/optimize/__init__.py
@@ -0,0 +1,6 @@
+"""
+Initialization of the optimize subpackage
+"""
+
+from .scalar_maximization import maximize_scalar
+
diff --git a/quantecon/optimize/scalar_maximization.py b/quantecon/optimize/scalar_maximization.py
new file mode 100644
index 000000000..b2ddc5f15
--- /dev/null
+++ b/quantecon/optimize/scalar_maximization.py
@@ -0,0 +1,135 @@
+import numpy as np
+from numba import jit, njit
+
+@njit
+def maximize_scalar(func, a, b, xtol=1e-5, maxiter=500):
+    """
+    Uses a jitted version of the maximization routine from SciPy's fminbound.
+    The algorithm is identical except that it's been switched to maximization
+    rather than minimization, and the tests for convergence have been stripped
+    out to allow for jit compilation.
+
+    Note that the input function `func` must be jitted or the call will fail.
+
+    Parameters
+    ----------
+    maxiter : int, optional
+        Maximum number of iterations to perform.
+    xtol : float, optional
+        Absolute error in solution `xopt` acceptable for convergence.
+    func : jitted function
+    a : scalar
+        Lower bound for search
+    b : scalar
+        Upper bound for search
+
+    Returns
+    -------
+    fval : float
+        The maximum value attained
+    xf : float
+        The maxizer
+
+    Example
+    -------
+
+    ```
+        @njit
+        def f(x):
+            return -(x + 2.0)**2 + 1.0
+
+        fval, xf = maximize_scalar(f, -2, 2)
+    ```
+
+    """
+    maxfun = maxiter
+
+    sqrt_eps = np.sqrt(2.2e-16)
+    golden_mean = 0.5 * (3.0 - np.sqrt(5.0))
+
+    fulc = a + golden_mean * (b - a)
+    nfc, xf = fulc, fulc
+    rat = e = 0.0
+    x = xf
+    fx = -func(x)
+    num = 1
+    fmin_data = (1, xf, fx)
+
+    ffulc = fnfc = fx
+    xm = 0.5 * (a + b)
+    tol1 = sqrt_eps * np.abs(xf) + xtol / 3.0
+    tol2 = 2.0 * tol1
+
+    while (np.abs(xf - xm) > (tol2 - 0.5 * (b - a))):
+        golden = 1
+        # Check for parabolic fit
+        if np.abs(e) > tol1:
+            golden = 0
+            r = (xf - nfc) * (fx - ffulc)
+            q = (xf - fulc) * (fx - fnfc)
+            p = (xf - fulc) * q - (xf - nfc) * r
+            q = 2.0 * (q - r)
+            if q > 0.0:
+                p = -p
+            q = np.abs(q)
+            r = e
+            e = rat
+
+            # Check for acceptability of parabola
+            if ((np.abs(p) < np.abs(0.5*q*r)) and (p > q*(a - xf)) and
+                    (p < q * (b - xf))):
+                rat = (p + 0.0) / q
+                x = xf + rat
+
+                if ((x - a) < tol2) or ((b - x) < tol2):
+                    si = np.sign(xm - xf) + ((xm - xf) == 0)
+                    rat = tol1 * si
+            else:      # do a golden section step
+                golden = 1
+
+        if golden:  # Do a golden-section step
+            if xf >= xm:
+                e = a - xf
+            else:
+                e = b - xf
+            rat = golden_mean*e
+
+        if rat == 0:
+            si = np.sign(rat) + 1
+        else:
+            si = np.sign(rat)
+
+        x = xf + si * np.maximum(np.abs(rat), tol1)
+        fu = -func(x)
+        num += 1
+        fmin_data = (num, x, fu)
+
+        if fu <= fx:
+            if x >= xf:
+                a = xf
+            else:
+                b = xf
+            fulc, ffulc = nfc, fnfc
+            nfc, fnfc = xf, fx
+            xf, fx = x, fu
+        else:
+            if x < xf:
+                a = x
+            else:
+                b = x
+            if (fu <= fnfc) or (nfc == xf):
+                fulc, ffulc = nfc, fnfc
+                nfc, fnfc = x, fu
+            elif (fu <= ffulc) or (fulc == xf) or (fulc == nfc):
+                fulc, ffulc = x, fu
+
+        xm = 0.5 * (a + b)
+        tol1 = sqrt_eps * np.abs(xf) + xtol / 3.0
+        tol2 = 2.0 * tol1
+
+        if num >= maxfun:
+            break
+
+    fval = -fx
+
+    return fval, xf
diff --git a/quantecon/optimize/tests/test_scalar_max.py b/quantecon/optimize/tests/test_scalar_max.py
new file mode 100644
index 000000000..9e09fb507
--- /dev/null
+++ b/quantecon/optimize/tests/test_scalar_max.py
@@ -0,0 +1,39 @@
+"""
+Tests for scalar maximization.
+
+"""
+import numpy as np
+from numpy.testing import assert_almost_equal
+from numba import njit
+
+from quantecon.optimize import maximize_scalar
+
+@njit
+def f(x):
+    """
+    A function for testing on.
+    """
+    return -(x + 2.0)**2 + 1.0
+
+def test_maximize_scalar():
+    """
+    Uses the function f defined above to test the scalar maximization 
+    routine.
+    """
+    true_fval = 1.0
+    true_xf = -2.0
+    fval, xf = maximize_scalar(f, -2, 2)
+    assert_almost_equal(true_fval, fval, decimal=4)
+    assert_almost_equal(true_xf, xf, decimal=4)
+
+
+if __name__ == '__main__':
+    import sys
+    import nose
+
+    argv = sys.argv[:]
+    argv.append('--verbose')
+    argv.append('--nocapture')
+    nose.main(argv=argv, defaultTest=__file__)
+
+
diff --git a/quantecon/version.py b/quantecon/version.py
index e38e023ca..aad6dede3 100644
--- a/quantecon/version.py
+++ b/quantecon/version.py
@@ -1,4 +1,4 @@
 """
 This is a VERSION file and should NOT be manually altered
 """
-version = '0.3.7'
\ No newline at end of file
+version = '0.3.8'
\ No newline at end of file

From 43845796a1bbb51002d89fc41886588126171c1e Mon Sep 17 00:00:00 2001
From: Natasha <tashawatkins@gmail.com>
Date: Thu, 7 Jun 2018 14:18:09 +0200
Subject: [PATCH 02/12] add args arguments

---
 quantecon/optimize/scalar_maximization.py   | 17 ++++++++++-------
 quantecon/optimize/tests/test_scalar_max.py | 19 +++++++++++++++++++
 2 files changed, 29 insertions(+), 7 deletions(-)

diff --git a/quantecon/optimize/scalar_maximization.py b/quantecon/optimize/scalar_maximization.py
index b2ddc5f15..f39bcf4c9 100644
--- a/quantecon/optimize/scalar_maximization.py
+++ b/quantecon/optimize/scalar_maximization.py
@@ -2,7 +2,7 @@
 from numba import jit, njit
 
 @njit
-def maximize_scalar(func, a, b, xtol=1e-5, maxiter=500):
+def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
     """
     Uses a jitted version of the maximization routine from SciPy's fminbound.
     The algorithm is identical except that it's been switched to maximization
@@ -13,15 +13,17 @@ def maximize_scalar(func, a, b, xtol=1e-5, maxiter=500):
 
     Parameters
     ----------
-    maxiter : int, optional
-        Maximum number of iterations to perform.
-    xtol : float, optional
-        Absolute error in solution `xopt` acceptable for convergence.
     func : jitted function
     a : scalar
         Lower bound for search
     b : scalar
         Upper bound for search
+    args : tuple, optional
+        Extra arguments passed to the objective function.
+    maxiter : int, optional
+        Maximum number of iterations to perform.
+    xtol : float, optional
+        Absolute error in solution `xopt` acceptable for convergence.
 
     Returns
     -------
@@ -42,6 +44,7 @@ def f(x):
     ```
 
     """
+    
     maxfun = maxiter
 
     sqrt_eps = np.sqrt(2.2e-16)
@@ -51,7 +54,7 @@ def f(x):
     nfc, xf = fulc, fulc
     rat = e = 0.0
     x = xf
-    fx = -func(x)
+    fx = -func(x, *args)
     num = 1
     fmin_data = (1, xf, fx)
 
@@ -100,7 +103,7 @@ def f(x):
             si = np.sign(rat)
 
         x = xf + si * np.maximum(np.abs(rat), tol1)
-        fu = -func(x)
+        fu = -func(x, *args)
         num += 1
         fmin_data = (num, x, fu)
 
diff --git a/quantecon/optimize/tests/test_scalar_max.py b/quantecon/optimize/tests/test_scalar_max.py
index 9e09fb507..448f72946 100644
--- a/quantecon/optimize/tests/test_scalar_max.py
+++ b/quantecon/optimize/tests/test_scalar_max.py
@@ -25,6 +25,25 @@ def test_maximize_scalar():
     fval, xf = maximize_scalar(f, -2, 2)
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
+    
+@njit
+def g(x, y):
+    """
+    A multivariate function for testing on.
+    """
+    return -x**2 + y
+    
+def test_maximize_scalar_multivariate():
+    """
+    Uses the function f defined above to test the scalar maximization 
+    routine.
+    """
+    y = 5
+    true_fval = 5.0
+    true_xf = -0.0
+    fval, xf = maximize_scalar(g, -10, 10, args=(y,))
+    assert_almost_equal(true_fval, fval, decimal=4)
+    assert_almost_equal(true_xf, xf, decimal=4)
 
 
 if __name__ == '__main__':

From c9933d3a4f510fde8fcd2c8900209a3a1d4843e2 Mon Sep 17 00:00:00 2001
From: Natasha <tashawatkins@gmail.com>
Date: Thu, 7 Jun 2018 15:14:52 +0200
Subject: [PATCH 03/12] fix typo

---
 quantecon/optimize/scalar_maximization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/quantecon/optimize/scalar_maximization.py b/quantecon/optimize/scalar_maximization.py
index f39bcf4c9..51b42fbd9 100644
--- a/quantecon/optimize/scalar_maximization.py
+++ b/quantecon/optimize/scalar_maximization.py
@@ -30,7 +30,7 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
     fval : float
         The maximum value attained
     xf : float
-        The maxizer
+        The maximizer
 
     Example
     -------

From 22dce1d1f11abfe2949713ef1efc70ee02bd68b1 Mon Sep 17 00:00:00 2001
From: Matt McKay <mamckay@gmail.com>
Date: Mon, 11 Jun 2018 10:38:32 +1000
Subject: [PATCH 04/12] update install requires in setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 2df3ab4c8..acc7c2c34 100644
--- a/setup.py
+++ b/setup.py
@@ -113,7 +113,7 @@ def write_version_py(filename=None):
       download_url='https://github.com/QuantEcon/QuantEcon.py/tarball/' + VERSION,
       keywords=['quantitative', 'economics'],
       install_requires=[
-          'numba>=0.36.2',
+          'numba>=0.38',
           'numpy',
           'scipy>=1.0.0',
           'sympy',

From e890f71279dfcb21100e99f44a5258ab934ce999 Mon Sep 17 00:00:00 2001
From: John Stachurski <john.stachurski@gmail.com>
Date: Mon, 25 Jun 2018 16:55:53 +1000
Subject: [PATCH 05/12] added status_flag to scalar maximizer

---
 quantecon/optimize/scalar_maximization.py   | 11 +++++++----
 quantecon/optimize/tests/test_scalar_max.py |  4 ++--
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/quantecon/optimize/scalar_maximization.py b/quantecon/optimize/scalar_maximization.py
index 51b42fbd9..04130f34e 100644
--- a/quantecon/optimize/scalar_maximization.py
+++ b/quantecon/optimize/scalar_maximization.py
@@ -31,6 +31,9 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
         The maximum value attained
     xf : float
         The maximizer
+    status_flag : int
+        Indicates whether or not the maximum number of function calls was
+        attained.  A value of 0 implies that the maximum was not hit.  
 
     Example
     -------
@@ -40,12 +43,13 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
         def f(x):
             return -(x + 2.0)**2 + 1.0
 
-        fval, xf = maximize_scalar(f, -2, 2)
+        fval, xf, status_flag = maximize_scalar(f, -2, 2)
     ```
 
     """
     
     maxfun = maxiter
+    status_flag = 0
 
     sqrt_eps = np.sqrt(2.2e-16)
     golden_mean = 0.5 * (3.0 - np.sqrt(5.0))
@@ -56,7 +60,6 @@ def f(x):
     x = xf
     fx = -func(x, *args)
     num = 1
-    fmin_data = (1, xf, fx)
 
     ffulc = fnfc = fx
     xm = 0.5 * (a + b)
@@ -105,7 +108,6 @@ def f(x):
         x = xf + si * np.maximum(np.abs(rat), tol1)
         fu = -func(x, *args)
         num += 1
-        fmin_data = (num, x, fu)
 
         if fu <= fx:
             if x >= xf:
@@ -131,8 +133,9 @@ def f(x):
         tol2 = 2.0 * tol1
 
         if num >= maxfun:
+            status_flag = 1
             break
 
     fval = -fx
 
-    return fval, xf
+    return fval, xf, status_flag
diff --git a/quantecon/optimize/tests/test_scalar_max.py b/quantecon/optimize/tests/test_scalar_max.py
index 448f72946..eadf08d3a 100644
--- a/quantecon/optimize/tests/test_scalar_max.py
+++ b/quantecon/optimize/tests/test_scalar_max.py
@@ -22,7 +22,7 @@ def test_maximize_scalar():
     """
     true_fval = 1.0
     true_xf = -2.0
-    fval, xf = maximize_scalar(f, -2, 2)
+    fval, xf, status_flag = maximize_scalar(f, -2, 2)
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
     
@@ -41,7 +41,7 @@ def test_maximize_scalar_multivariate():
     y = 5
     true_fval = 5.0
     true_xf = -0.0
-    fval, xf = maximize_scalar(g, -10, 10, args=(y,))
+    fval, xf, status_flag = maximize_scalar(g, -10, 10, args=(y,))
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
 

From f722b25192fd6fb6647a68411087dd7a04a4ff2c Mon Sep 17 00:00:00 2001
From: John Stachurski <john.stachurski@gmail.com>
Date: Mon, 25 Jun 2018 17:47:37 +1000
Subject: [PATCH 06/12] modified return value to include number of function
 calls

---
 quantecon/optimize/scalar_maximization.py   | 11 +++++++----
 quantecon/optimize/tests/test_scalar_max.py |  4 ++--
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/quantecon/optimize/scalar_maximization.py b/quantecon/optimize/scalar_maximization.py
index 04130f34e..68b415846 100644
--- a/quantecon/optimize/scalar_maximization.py
+++ b/quantecon/optimize/scalar_maximization.py
@@ -31,9 +31,11 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
         The maximum value attained
     xf : float
         The maximizer
-    status_flag : int
-        Indicates whether or not the maximum number of function calls was
+    info : tuple
+        A tuple of the form (status_flag, num_iter).  Here status_flag
+        indicates whether or not the maximum number of function calls was
         attained.  A value of 0 implies that the maximum was not hit.  
+        The value `num_iter` is the number of function calls.
 
     Example
     -------
@@ -43,7 +45,7 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
         def f(x):
             return -(x + 2.0)**2 + 1.0
 
-        fval, xf, status_flag = maximize_scalar(f, -2, 2)
+        fval, xf, info = maximize_scalar(f, -2, 2)
     ```
 
     """
@@ -137,5 +139,6 @@ def f(x):
             break
 
     fval = -fx
+    info = status_flag, num
 
-    return fval, xf, status_flag
+    return fval, xf, info
diff --git a/quantecon/optimize/tests/test_scalar_max.py b/quantecon/optimize/tests/test_scalar_max.py
index eadf08d3a..a1ddd4d5f 100644
--- a/quantecon/optimize/tests/test_scalar_max.py
+++ b/quantecon/optimize/tests/test_scalar_max.py
@@ -22,7 +22,7 @@ def test_maximize_scalar():
     """
     true_fval = 1.0
     true_xf = -2.0
-    fval, xf, status_flag = maximize_scalar(f, -2, 2)
+    fval, xf, info = maximize_scalar(f, -2, 2)
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
     
@@ -41,7 +41,7 @@ def test_maximize_scalar_multivariate():
     y = 5
     true_fval = 5.0
     true_xf = -0.0
-    fval, xf, status_flag = maximize_scalar(g, -10, 10, args=(y,))
+    fval, xf, info = maximize_scalar(g, -10, 10, args=(y,))
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
 

From 70be9530f3808540694d456a4fe83859d59a0537 Mon Sep 17 00:00:00 2001
From: John Stachurski <john.stachurski@gmail.com>
Date: Mon, 25 Jun 2018 18:40:05 +1000
Subject: [PATCH 07/12] modified return value to accommodate feedback and
 changed function name to brent_max

---
 quantecon/optimize/__init__.py              |  2 +-
 quantecon/optimize/scalar_maximization.py   | 10 +++++-----
 quantecon/optimize/tests/test_scalar_max.py | 10 +++++-----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/quantecon/optimize/__init__.py b/quantecon/optimize/__init__.py
index ea25f0199..4937fb738 100644
--- a/quantecon/optimize/__init__.py
+++ b/quantecon/optimize/__init__.py
@@ -2,5 +2,5 @@
 Initialization of the optimize subpackage
 """
 
-from .scalar_maximization import maximize_scalar
+from .scalar_maximization import brent_max
 
diff --git a/quantecon/optimize/scalar_maximization.py b/quantecon/optimize/scalar_maximization.py
index 68b415846..9eff745fb 100644
--- a/quantecon/optimize/scalar_maximization.py
+++ b/quantecon/optimize/scalar_maximization.py
@@ -2,7 +2,7 @@
 from numba import jit, njit
 
 @njit
-def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
+def brent_max(func, a, b, args=(), xtol=1e-5, maxiter=500):
     """
     Uses a jitted version of the maximization routine from SciPy's fminbound.
     The algorithm is identical except that it's been switched to maximization
@@ -27,10 +27,10 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
 
     Returns
     -------
-    fval : float
-        The maximum value attained
     xf : float
         The maximizer
+    fval : float
+        The maximum value attained
     info : tuple
         A tuple of the form (status_flag, num_iter).  Here status_flag
         indicates whether or not the maximum number of function calls was
@@ -45,7 +45,7 @@ def maximize_scalar(func, a, b, args=(), xtol=1e-5, maxiter=500):
         def f(x):
             return -(x + 2.0)**2 + 1.0
 
-        fval, xf, info = maximize_scalar(f, -2, 2)
+        xf, fval, info = maximize_scalar(f, -2, 2)
     ```
 
     """
@@ -141,4 +141,4 @@ def f(x):
     fval = -fx
     info = status_flag, num
 
-    return fval, xf, info
+    return xf, fval, info
diff --git a/quantecon/optimize/tests/test_scalar_max.py b/quantecon/optimize/tests/test_scalar_max.py
index a1ddd4d5f..b05c00113 100644
--- a/quantecon/optimize/tests/test_scalar_max.py
+++ b/quantecon/optimize/tests/test_scalar_max.py
@@ -6,7 +6,7 @@
 from numpy.testing import assert_almost_equal
 from numba import njit
 
-from quantecon.optimize import maximize_scalar
+from quantecon.optimize import brent_max
 
 @njit
 def f(x):
@@ -15,14 +15,14 @@ def f(x):
     """
     return -(x + 2.0)**2 + 1.0
 
-def test_maximize_scalar():
+def test_brent_max():
     """
     Uses the function f defined above to test the scalar maximization 
     routine.
     """
     true_fval = 1.0
     true_xf = -2.0
-    fval, xf, info = maximize_scalar(f, -2, 2)
+    xf, fval, info = brent_max(f, -2, 2)
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
     
@@ -33,7 +33,7 @@ def g(x, y):
     """
     return -x**2 + y
     
-def test_maximize_scalar_multivariate():
+def test_brent_max():
     """
     Uses the function f defined above to test the scalar maximization 
     routine.
@@ -41,7 +41,7 @@ def test_maximize_scalar_multivariate():
     y = 5
     true_fval = 5.0
     true_xf = -0.0
-    fval, xf, info = maximize_scalar(g, -10, 10, args=(y,))
+    xf, fval, info = brent_max(g, -10, 10, args=(y,))
     assert_almost_equal(true_fval, fval, decimal=4)
     assert_almost_equal(true_xf, xf, decimal=4)
 

From 44df5dfd6e39f01208c155c0d0c8aa7a92e1f333 Mon Sep 17 00:00:00 2001
From: Matt McKay <mamckay@gmail.com>
Date: Tue, 26 Jun 2018 11:02:08 +1000
Subject: [PATCH 08/12] add optimize subpackage to docs

---
 docs/qe_apidoc.py | 30 ++++++++++++++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

diff --git a/docs/qe_apidoc.py b/docs/qe_apidoc.py
index ffc345b5e..582bdcef1 100644
--- a/docs/qe_apidoc.py
+++ b/docs/qe_apidoc.py
@@ -80,6 +80,15 @@
     :show-inheritance:
 """
 
+optimize_module_template = """{mod_name}
+{equals}
+
+.. automodule:: quantecon.optimize.{mod_name}
+    :members:
+    :undoc-members:
+    :show-inheritance:
+"""
+
 random_module_template = """{mod_name}
 {equals}
 
@@ -211,6 +220,12 @@ def model_tool():
     # Alphabetize
     markov.sort()
 
+    # list file names with optimize
+    optimize_files = glob("../quantecon/optimize/[a-z0-9]*.py")
+    optimize = list(map(lambda x: x.split('/')[-1][:-3], optimize_files))
+    # Alphabetize
+    optimize.sort()
+
     # list file names with random
     random_files = glob("../quantecon/random/[a-z0-9]*.py")
     random = list(map(lambda x: x.split('/')[-1][:-3], random_files))
@@ -230,7 +245,7 @@ def model_tool():
     # Alphabetize
     util.sort()
 
-    for folder in ["game_theory", "markov", "random", "tools", "util"]:
+    for folder in ["game_theory", "markov", "optimize", "random", "tools", "util"]:
         if not os.path.exists(source_join(folder)):
             os.makedirs(source_join(folder))
 
@@ -257,6 +272,13 @@ def model_tool():
             equals = "=" * len(mod)
             f.write(markov_module_template.format(mod_name=mod, equals=equals))
 
+    # Write file for each optimize file
+    for mod in optimize:
+        new_path = os.path.join("source", "optimize", mod + ".rst")
+        with open(new_path, "w") as f:
+            equals = "=" * len(mod)
+            f.write(optimize_module_template.format(mod_name=mod, equals=equals))
+
     # Write file for each random file
     for mod in random:
         new_path = os.path.join("source", "random", mod + ".rst")
@@ -284,24 +306,28 @@ def model_tool():
 
     gt = "game_theory/" + "\n   game_theory/".join(game_theory)
     mark = "markov/" + "\n   markov/".join(markov)
+    opti = "optimize/" + "\n   optimize/".join(optimize)
     rand = "random/" + "\n   random/".join(random)
     tlz = "tools/" + "\n   tools/".join(tools)
     utls = "util/" + "\n   util/".join(util)
     #-TocTree-#
     toc_tree_list = {"game_theory": gt,
                      "markov": mark,
+                     "optimize" : opti,
                      "tools": tlz,
                      "random": rand,
                      "util": utls,
                      }
 
-    for f_name in ("game_theory", "markov", "random", "tools", "util"):
+    for f_name in ("game_theory", "markov", "optimize", "random", "tools", "util"):
         with open(source_join(f_name + ".rst"), "w") as f:
             m_name = f_name
             if f_name == "game_theory":
                 f_name = "Game Theory"                                             #Produce Nicer Title for Game Theory Module
             if f_name == "util":
                 f_name = "Utilities"            #Produce Nicer Title for Utilities Module
+            if f_name == "optimize":
+                f_name = "Optimize"
             temp = split_file_template.format(name=f_name.capitalize(),
                                               equals="="*len(f_name),
                                               files=toc_tree_list[m_name])

From 75268959cadc9c3455e0ea4404470e90a629255a Mon Sep 17 00:00:00 2001
From: Matt McKay <mamckay@gmail.com>
Date: Tue, 26 Jun 2018 11:32:06 +1000
Subject: [PATCH 09/12] add optimize to top level index and setup for
 installation

---
 docs/source/index.rst | 1 +
 setup.py              | 1 +
 2 files changed, 2 insertions(+)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 9e969cea5..6dcf25317 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ mainly used by developers internal to the package.
 
    game_theory
    markov
+   optimize
    random
    tools
    util
diff --git a/setup.py b/setup.py
index acc7c2c34..efbe6f05c 100644
--- a/setup.py
+++ b/setup.py
@@ -98,6 +98,7 @@ def write_version_py(filename=None):
                 'quantecon.game_theory',
                 'quantecon.game_theory.game_generators',
                 'quantecon.markov',
+                'quantecon.optimize',
                 'quantecon.random',
                 'quantecon.tests',
                 'quantecon.util',

From 7f24600713b94004b5503a02e2b669457a46b784 Mon Sep 17 00:00:00 2001
From: Matt McKay <mamckay@gmail.com>
Date: Tue, 26 Jun 2018 11:59:38 +1000
Subject: [PATCH 10/12] add tracked generated sources

---
 docs/source/index.rst                        | 1 -
 docs/source/optimize.rst                     | 7 +++++++
 docs/source/optimize/scalar_maximization.rst | 7 +++++++
 3 files changed, 14 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/optimize.rst
 create mode 100644 docs/source/optimize/scalar_maximization.rst

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 6dcf25317..9e969cea5 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,7 +13,6 @@ mainly used by developers internal to the package.
 
    game_theory
    markov
-   optimize
    random
    tools
    util
diff --git a/docs/source/optimize.rst b/docs/source/optimize.rst
new file mode 100644
index 000000000..7aa11d50d
--- /dev/null
+++ b/docs/source/optimize.rst
@@ -0,0 +1,7 @@
+Optimize
+========
+
+.. toctree::
+   :maxdepth: 2
+
+   optimize/scalar_maximization
diff --git a/docs/source/optimize/scalar_maximization.rst b/docs/source/optimize/scalar_maximization.rst
new file mode 100644
index 000000000..af15d0538
--- /dev/null
+++ b/docs/source/optimize/scalar_maximization.rst
@@ -0,0 +1,7 @@
+scalar_maximization
+===================
+
+.. automodule:: quantecon.optimize.scalar_maximization
+    :members:
+    :undoc-members:
+    :show-inheritance:

From d221eb1b3cdf5de3f83b0f326cedc46d08b965c3 Mon Sep 17 00:00:00 2001
From: Matt McKay <mamckay@gmail.com>
Date: Tue, 26 Jun 2018 14:11:05 +1000
Subject: [PATCH 11/12] fix index generation

---
 docs/qe_apidoc.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/qe_apidoc.py b/docs/qe_apidoc.py
index 582bdcef1..3c4c87c9d 100644
--- a/docs/qe_apidoc.py
+++ b/docs/qe_apidoc.py
@@ -142,6 +142,7 @@
 
    game_theory
    markov
+   optimize
    random
    tools
    util

From 09b553196a7334a5fca84fbd9b5606f48e97eca1 Mon Sep 17 00:00:00 2001
From: Matt McKay <mamckay@gmail.com>
Date: Tue, 26 Jun 2018 14:21:53 +1000
Subject: [PATCH 12/12] add tracked generated source files

---
 docs/source/index.rst        | 1 +
 docs/source/tools.rst        | 1 +
 docs/source/tools/filter.rst | 7 +++++++
 3 files changed, 9 insertions(+)
 create mode 100644 docs/source/tools/filter.rst

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 9e969cea5..6dcf25317 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ mainly used by developers internal to the package.
 
    game_theory
    markov
+   optimize
    random
    tools
    util
diff --git a/docs/source/tools.rst b/docs/source/tools.rst
index ef3923f93..7b8456710 100644
--- a/docs/source/tools.rst
+++ b/docs/source/tools.rst
@@ -11,6 +11,7 @@ Tools
    tools/distributions
    tools/ecdf
    tools/estspec
+   tools/filter
    tools/graph_tools
    tools/gridtools
    tools/ivp
diff --git a/docs/source/tools/filter.rst b/docs/source/tools/filter.rst
new file mode 100644
index 000000000..f102fb19b
--- /dev/null
+++ b/docs/source/tools/filter.rst
@@ -0,0 +1,7 @@
+filter
+======
+
+.. automodule:: quantecon.filter
+    :members:
+    :undoc-members:
+    :show-inheritance: