Explain bounds are non-relaxable in documentation, don't change x0 if feasible (but close to bounds), add gfortran dependency to documentation, update examples to remove random seed requirement. This closes #6 and closes #8.

Author: lindonroberts

README.rst

@@ -39,6 +39,7 @@ Requirements
 DFO-LS requires the following software to be installed:
 
 * Python 2.7 or Python 3 (http://www.python.org/)
+* Fortran compiler (e.g. gfortran)
 
 Additionally, the following python packages should be installed (these will be installed automatically if using *pip*, see `Installation using pip`_):
 

dfols/solver.py

@@ -941,21 +941,11 @@ def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8,
         return results
 
     # Enforce lower & upper bounds on x0
-    idx = (xl < x0) & (x0 <= xl + rhobeg)
-    if np.any(idx):
-        warnings.warn("x0 too close to lower bound, adjusting", RuntimeWarning)
-    x0[idx] = xl[idx] + rhobeg
-
     idx = (x0 <= xl)
     if np.any(idx):
         warnings.warn("x0 below lower bound, adjusting", RuntimeWarning)
     x0[idx] = xl[idx]
 
-    idx = (xu - rhobeg <= x0) & (x0 < xu)
-    if np.any(idx):
-        warnings.warn("x0 too close to upper bound, adjusting", RuntimeWarning)
-    x0[idx] = xu[idx] - rhobeg
-
     idx = (x0 >= xu)
     if np.any(idx):
         warnings.warn("x0 above upper bound, adjusting", RuntimeWarning)

dfols/version.py

@@ -22,4 +22,4 @@
 
 """
 
-__version__ = '1.1.1'
+__version__ = '1.2'

docs/history.rst

@@ -24,3 +24,11 @@ Version 1.1.1 (5 Apr 2019)
 --------------------------
 * Link code to Zenodo, to create DOI - no changes to the DFO-LS algorithm.
 
+Version 1.2
+-----------
+* Use deterministic initialisation by default (so it is no longer necessary to set a random seed for reproducibility of DFO-LS results).
+* Full model Hessian stored rather than just upper triangular part - this improves the runtime of Hessian-based operations.
+* Faster trust-region and geometry subproblem solutions in Fortran using the `trustregion <https://github.com/lindonroberts/trust-region>`_ package.
+* Faster interpolation solution for multiple right-hand sides.
+* Don't adjust starting point if it is close to the bounds (as long as it is feasible).
+* Bugfix: correctly handle 1-sided bounds as inputs.

docs/index.rst

@@ -20,6 +20,8 @@ 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
 
+The upper and lower bounds on the variables are non-relaxable (i.e. DFO-LS will never ask to evaluate a point outside the bounds).
+
 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 <https://arxiv.org/abs/1804.00154>`_, 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.

docs/install.rst

@@ -6,6 +6,7 @@ Requirements
 DFO-LS requires the following software to be installed:
 
 * Python 2.7 or Python 3 (http://www.python.org/)
+* Fortran compiler (e.g. gfortran)
 
 Additionally, the following python packages should be installed (these will be installed automatically if using *pip*, see `Installation using pip`_):
 

docs/userguide.rst

@@ -11,7 +11,7 @@ DFO-LS is designed to solve the local optimization problem
    \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
 
-where the bound constraints :math:`a \leq x \leq b` are optional.
+where the bound constraints :math:`a \leq x \leq b` are optional. The upper and lower bounds on the variables are non-relaxable (i.e. DFO-LS will never ask to evaluate a point outside the bounds).
 
 DFO-LS iteratively constructs an interpolation-based model for the objective, and determines a step using a trust-region framework.
 For an in-depth technical description of the algorithm see the paper [CFMR2018]_.

examples/data_fitting.py

@@ -18,9 +18,6 @@ def prediction_error(x):
 # Define the starting point
 x0 = np.array([100.0, -1.0])
 
-# Set random seed (for reproducibility)
-np.random.seed(0)
-
 # We expect exponential decay: set upper bound x[1] <= 0
 upper = np.array([1e20, 0.0])
 

examples/nonlinear_system.py

@@ -18,9 +18,6 @@ def nonlinear_system(x):
 #          DFO-LS returns will likely depend on x0!
 x0 = np.array([0.1, -2.0])
 
-# Set random seed (for reproducibility)
-np.random.seed(0)
-
 # Call DFO-LS
 soln = dfols.solve(nonlinear_system, x0)
 

examples/rosenbrock_basic.py

@@ -10,9 +10,6 @@ def rosenbrock(x):
 # Define the starting point
 x0 = np.array([-1.2, 1.0])
 
-# Set random seed (for reproducibility)
-np.random.seed(0)
-
 # For optional extra output details
 # import logging
 # logging.basicConfig(level=logging.INFO, format='%(message)s')