Added link to paper to README and documentation

Author: lindonroberts

README.rst

@@ -16,7 +16,7 @@ DFO-LS: Derivative-Free Optimizer for Least-Squares
 
 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). DFO-LS is more flexible version of `DFO-GN <https://github.com/numericalalgorithmsgroup/dfogn>`_.
+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 <https://arxiv.org/abs/1804.00154>`_, 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.
 

docs/advanced.rst

@@ -107,5 +107,5 @@ References
 ----------
 
 .. [CFMR2018]   
-   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).
+   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).
 

docs/index.rst

@@ -20,7 +20,7 @@ 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). 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 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/info.rst

@@ -90,5 +90,5 @@ References
 ----------
 
 .. [CFMR2018]   
-   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).
+   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).
 

docs/userguide.rst

@@ -469,5 +469,5 @@ References
 ----------
 
 .. [CFMR2018]   
-   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).
+   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).