Added new paper citation information

Author: lindonroberts

README.rst

@@ -38,6 +38,10 @@ If you use DFO-LS in a paper, please cite:
 
 Cartis, C., Fiala, J., Marteau, B. and Roberts, L., `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41.
 
+If you use DFO-LS for problems with constraints, including bound constraints, please also cite:
+
+Hough, M. and Roberts, L., `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://arxiv.org/abs/2111.05443>`_, *arXiv preprint arXiv:2111.05443*, (2021).
+
 Requirements
 ------------
 DFO-LS requires the following software to be installed:

docs/advanced.rst

@@ -2,7 +2,7 @@ Advanced Usage
 ==============
 This section describes different optional user parameters available in DFO-LS.
 
-In the last section (:doc:`userguide`), we introduced :code:`dfols.solve()`, which has the optional input :code:`user_params`. This is a Python dictionary of user parameters. We will now go through the settings which can be changed in this way. More details are available in the paper [CFMR2018]_.
+In the last section (:doc:`userguide`), we introduced :code:`dfols.solve()`, which has the optional input :code:`user_params`. This is a Python dictionary of user parameters. We will now go through the settings which can be changed in this way. More details are available in the papers [CFMR2018]_ and [HR2021]_.
 
 The default values, used if no override is given, in some cases vary depending on whether :code:`objfun` has stochastic noise; that is, whether evaluating :code:`objfun(x)` several times at the same :code:`x` gives the same result or not. Whether or not this is the case is determined by the :code:`objfun_has_noise` input to :code:`dfols.solve()` (and not by inspecting :code:`objfun`, for instance).
 
@@ -119,3 +119,5 @@ References
 .. [CFMR2018]   
    Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon Roberts, `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint <https://arxiv.org/abs/1804.00154>`_] 
 
+.. [HR2021]   
+   Hough, M. and Roberts, L., `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://arxiv.org/abs/2111.05443>`_, *arXiv preprint arXiv:2111.05443*, (2021).

docs/build/doctrees/advanced.doctree

@@ -2,7 +2,7 @@ Advanced Usage
 ==============
 This section describes different optional user parameters available in DFO-LS.
 
-In the last section (:doc:`userguide`), we introduced :code:`dfols.solve()`, which has the optional input :code:`user_params`. This is a Python dictionary of user parameters. We will now go through the settings which can be changed in this way. More details are available in the paper [CFMR2018]_.
+In the last section (:doc:`userguide`), we introduced :code:`dfols.solve()`, which has the optional input :code:`user_params`. This is a Python dictionary of user parameters. We will now go through the settings which can be changed in this way. More details are available in the papers [CFMR2018]_ and [HR2021]_.
 
 The default values, used if no override is given, in some cases vary depending on whether :code:`objfun` has stochastic noise; that is, whether evaluating :code:`objfun(x)` several times at the same :code:`x` gives the same result or not. Whether or not this is the case is determined by the :code:`objfun_has_noise` input to :code:`dfols.solve()` (and not by inspecting :code:`objfun`, for instance).
 
@@ -119,3 +119,5 @@ References
 .. [CFMR2018]   
    Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon Roberts, `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint <https://arxiv.org/abs/1804.00154>`_] 
 
+.. [HR2021]   
+   Hough, M. and Roberts, L., `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://arxiv.org/abs/2111.05443>`_, *arXiv preprint arXiv:2111.05443*, (2021).

docs/build/doctrees/environment.pickle

@@ -23,7 +23,12 @@ That is, DFO-LS solves
 
 The constraint set :math:`C` is the intersection of multiple convex sets provided as input by the user. All constraints are non-relaxable (i.e. DFO-LS will never ask to evaluate a point that is not feasible).
 
-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://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint <https://arxiv.org/abs/1804.00154>`_] . DFO-LS is a more flexible version of `DFO-GN <https://github.com/numericalalgorithmsgroup/dfogn>`_.
+Full details of the DFO-LS algorithm are given in our papers: 
+
+* C. Cartis, J. Fiala, B. Marteau and L. Roberts, `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint <https://arxiv.org/abs/1804.00154>`_] . 
+* Hough, M. and Roberts, L., `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://arxiv.org/abs/2111.05443>`_, *arXiv preprint arXiv:2111.05443*, (2021).
+
+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/build/doctrees/index.doctree

@@ -86,11 +86,13 @@ At each step, we compute a trial step :math:`s_k` designed to make our approxima
 
 In DFO-LS, we construct our approximation :math:`m_k(s)` by interpolating a linear approximation for each residual :math:`r_i(x)` at several points close to :math:`x_k`. To make sure our interpolated model is accurate, we need to regularly check that the points are well-spaced, and move them if they aren't (i.e. improve the geometry of our interpolation points).
 
-A complete description of the DFO-LS algorithm is given in our paper [CFMR2018]_.
+A complete description of the DFO-LS algorithm is given in our papers [CFMR2018]_ and [HR2021]_.
 
 References
 ----------
 
 .. [CFMR2018]   
    Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon Roberts, `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint <https://arxiv.org/abs/1804.00154>`_] 
 
+.. [HR2021]   
+   Hough, M. and Roberts, L., `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://arxiv.org/abs/2111.05443>`_, *arXiv preprint arXiv:2111.05443*, (2021).