Bug fix in trust region subproblem - upgrade to version 1.0.1

README.rst

@@ -1,9 +1,11 @@
 ==================================================================
 DFO-LS: Derivative-Free Optimizer for Least-Squares |PyPI Version|
 ==================================================================
-DFO-LS is a flexible package for solving nonlinear least-squares minimisation, without requiring derivatives of the objective.
+DFO-LS is a flexible package for solving nonlinear least-squares minimisation, without requiring derivatives of the objective. It is particularly useful when evaluations of the objective function are expensive and/or noisy.
 
-This is an implementation of the algorithm from our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, technical report, University of Oxford, (2018).
+This is an implementation of the algorithm from our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, technical report, University of Oxford, (2018). DFO-LS is more flexible version of `DFO-GN <https://github.com/numericalalgorithmsgroup/dfogn>`_.
+
+If you are interested in solving general optimization problems (without a least-squares structure), you may wish to try `Py-BOBYQA <https://github.com/numericalalgorithmsgroup/pybobyqa>`_, which has many of the same features as DFO-LS.
 
 Documentation
 -------------
@@ -23,7 +25,7 @@ Additionally, the following python packages should be installed (these will be i
 
 Installation using pip
 ----------------------
-For easy installation, use *pip* (http://www.pip-installer.org/) as root::
+For easy installation, use `pip <http://www.pip-installer.org/>`_ as root::
 
     $ [sudo] pip install DFO-LS
 

dfols/tests/test_trust_region.py

@@ -92,7 +92,8 @@ def runTest(self):
         s_cauchy, red_cauchy, crvmin_cauchy = cauchy_pt(g, hess, Delta)
         self.assertTrue(est_min <= red_cauchy, 'Cauchy reduction not achieved')
         self.assertTrue(np.all(gnew == g + hess.vec_mul(d)), 'Wrong gnew')
-        self.assertAlmostEqual(crvmin, -1.0, 'Wrong crvmin')
+        print(crvmin)
+        self.assertAlmostEqual(crvmin, 1.2, 'Wrong crvmin')
 
 
 class TestUncBdry(unittest.TestCase):
@@ -210,7 +211,8 @@ def runTest(self):
         # print(d)
         self.assertTrue(est_min <= red_cauchy, 'Cauchy reduction not achieved')
         self.assertTrue(np.all(gnew == g + hess.vec_mul(d)), 'Wrong gnew')
-        self.assertAlmostEqual(crvmin, 1.5, 'Wrong crvmin')
+        print(crvmin)
+        self.assertAlmostEqual(crvmin, -1.0, 'Wrong crvmin')
 
 
 class TestConBdry(unittest.TestCase):
@@ -233,7 +235,8 @@ def runTest(self):
         s_cauchy, red_cauchy, crvmin_cauchy = cauchy_pt_box(g, hess, Delta, sl - xopt, su - xopt)
         self.assertTrue(est_min <= red_cauchy, 'Cauchy reduction not achieved')
         self.assertTrue(np.max(np.abs(gnew - g - hess.vec_mul(d))) < 1e-10, 'Wrong gnew')
-        self.assertAlmostEqual(crvmin, 1.0, 'Wrong crvmin')
+        print(crvmin)
+        self.assertAlmostEqual(crvmin, -1.0, 'Wrong crvmin')
         # self.assertAlmostEqual(crvmin, crvmin_cauchy, 'Wrong crvmin')
 
 

dfols/trust_region.py

@@ -153,7 +153,7 @@ def trsbox(xopt, g, hess, sl, su, delta):
 
         # Reduce STPLEN if necessary in order to preserve the simple bounds,
         # letting IACT be the index of the new constrained variable.
-        iact = -1
+        iact = None
         for i in range(n):
             if s[i] != 0.0:
                 temp = (su[i] - xopt[i] - d[i] if s[i] > 0.0 else sl[i] - xopt[i] - d[i]) / s[i]
@@ -166,8 +166,8 @@ def trsbox(xopt, g, hess, sl, su, delta):
         if stplen > 0.0:
             iterc += 1
             temp = shs / stepsq
-            if iact == 0 and temp > 0.0:
-                crvmin = (min(crvmin, temp) if crvmin != -1.0 else temp)
+            if iact is None and temp > 0.0:
+                crvmin = min(crvmin, temp) if crvmin != -1.0 else temp
             ggsav = gredsq
             gnew += stplen * hs
             d += stplen * s
@@ -176,7 +176,7 @@ def trsbox(xopt, g, hess, sl, su, delta):
             qred += sdec
 
         # Restart the conjugate gradient method if it has hit a new bound.
-        if iact > -1:
+        if iact is not None:
             nact += 1
             xbdi[iact] = (1 if s[iact] >= 0.0 else -1)
             delsq = delsq - d[iact] ** 2
@@ -253,7 +253,7 @@ def alt_trust_step(n, xopt, hess, sl, su, d, xbdi, nact, gnew, qred):
             # bound, there is a branch back to label 100 after fixing that variable.
             free_variable_reached_bound = False
             angbd = 1.0
-            iact = -1
+            iact = None
             for i in range(n):
                 if xbdi[i] == 0:
                     tempa = xopt[i] + d[i] - sl[i]
@@ -347,7 +347,7 @@ def alt_trust_step(n, xopt, hess, sl, su, d, xbdi, nact, gnew, qred):
             hred = cth * hred + sth * hs
 
             qred += sdec
-            if iact > -1 and isav == iu - 1:
+            if iact is not None and isav == iu - 1:
                 nact += 1
                 xbdi[iact] = xsav
                 restart_alt_loop = True

dfols/version.py

@@ -22,4 +22,4 @@
 
 """
 
-__version__ = '1.0'
+__version__ = '1.0.1'

docs/history.rst

@@ -0,0 +1,12 @@
+Version History
+===============
+This section lists the different versions of DFO-LS and the updates between them.
+
+Version 1.0 (6 Feb 2018)
+------------------------
+* Initial release of DFO-LS
+
+Version 1.0.1 (20 Feb 2018)
+---------------------------
+* Minor bug fix to trust region subproblem solver (the output :code:`crvmin` is calculated correctly) - this has minimal impact on the performance of DFO-LS.
+

docs/index.rst

@@ -20,7 +20,9 @@ That is, DFO-LS solves
    \min_{x\in\mathbb{R}^n}  &\quad  f(x) := \sum_{i=1}^{m}r_{i}(x)^2 \\
    \text{s.t.} &\quad  a \leq x \leq b
 
-Full details of the DFO-LS algorithm are given in our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, technical report, University of Oxford, (2018).
+Full details of the DFO-LS algorithm are given in our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, technical report, University of Oxford, (2018). DFO-LS is a more flexible version of `DFO-GN <https://github.com/numericalalgorithmsgroup/dfogn>`_.
+
+If you are interested in solving general optimization problems (without a least-squares structure), you may wish to try `Py-BOBYQA <https://github.com/numericalalgorithmsgroup/pybobyqa>`_, which has many of the same features as DFO-LS.
 
 DFO-LS is released under the GNU General Public License. Please `contact NAG <http://www.nag.com/content/worldwide-contact-information>`_ for alternative licensing.
 
@@ -33,6 +35,7 @@ DFO-LS is released under the GNU General Public License. Please `contact NAG <ht
    userguide
    advanced
    diagnostic
+   history
 
 Acknowledgements
 ----------------

docs/userguide.rst

@@ -162,9 +162,9 @@ DFO-LS correctly finds the solution to the constrained problem:
       Solution xmin = [ 0.9   0.81]
       Residual vector = [ 0.   0.1]
       Objective value f(xmin) = 0.01
-      Needed 64 objective evaluations (at 64 points)
+      Needed 65 objective evaluations (at 65 points)
       Approximate Jacobian = [[ -1.79999998e+01   9.99999990e+00]
-       [ -1.00000000e+00  -1.26970349e-09]]
+       [ -9.99999998e-01  -2.53940698e-09]]
       Exit flag = 0
       Success: rho has reached rhoend
       ****************************
@@ -196,8 +196,8 @@ And we can now see each evaluation of :code:`objfun`:
       Function eval 2 at point 2 has f = 14.337296 at x = [-1.08  0.85]
       Function eval 3 at point 3 has f = 55.25 at x = [-1.2   0.73]
       ...
-      Function eval 63 at point 63 has f = 0.0100000029949496 at x = [ 0.89999999  0.81      ]
-      Function eval 64 at point 64 has f = 0.00999999999999993 at x = [ 0.9   0.81]
+      Function eval 64 at point 64 has f = 0.0100000029949496 at x = [ 0.89999999  0.81      ]
+      Function eval 65 at point 65 has f = 0.00999999999999993 at x = [ 0.9   0.81]
       Did a total of 1 run(s)
 
 If we wanted to save this output to a file, we could replace the above call to :code:`logging.basicConfig()` with
@@ -453,11 +453,11 @@ The output of this is
   
       ****** DFO-LS Results ******
       Solution xmin = [ 0.09777309 -2.32510588]
-      Residual vector = [ -3.16191517e-13  -3.58602037e-12]
-      Objective value f(xmin) = 1.295951917e-23
-      Needed 17 objective evaluations (at 17 points)
-      Approximate Jacobian = [[  3.32510506   0.9022256 ]
-       [ 10.22775528  -1.00001417]]
+      Residual vector = [  2.89990254e-13   3.31557004e-12]
+      Objective value f(xmin) = 1.107709904e-23
+      Needed 18 objective evaluations (at 18 points)
+      Approximate Jacobian = [[  3.32510429   0.90222738]
+       [ 10.22774647  -0.9999939 ]]
       Exit flag = 0
       Success: Objective is sufficiently small
       ****************************

setup.py

@@ -35,7 +35,7 @@
     author='Lindon Roberts',
     author_email='[email protected]',
     url='https://github.com/numericalalgorithmsgroup/dfols/',
-    download_url='https://github.com/numericalalgorithmsgroup/dfols/archive/v1.0.tar.gz',
+    download_url='https://github.com/numericalalgorithmsgroup/dfols/archive/v1.0.1.tar.gz',
     packages=['dfols'],
     license='GNU GPL',
     keywords = 'mathematics derivative free optimization nonlinear least squares',