Merge pull request #484 from pymor/bibliography.bib

[docs] move all references to bibliography.rst
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diff --git a/docs/source/bibliography.rst b/docs/source/bibliography.rst
@@ -0,0 +1,97 @@
+************
+Bibliography
+************
+
+.. [A05]  A. C. Antoulas, Approximation of Large-Scale Dynamical
+          Systems,
+          SIAM, 2005.
+
+.. [ABG10] A. C. Antoulas, C. A. Beattie, S. Gugercin,
+           Interpolatory model reduction of large-scale dynamical
+           systems,
+           Efficient Modeling and Control of Large-Scale Systems,
+           Springer-Verlag, 2010.
+
+.. [BG09] C. A. Beattie, S. Gugercin, Interpolatory projection
+          methods for structure-preserving model reduction,
+          Systems & Control Letters 58, 2009
+
+.. [BG12] C. A. Beattie, S. Gugercin, Realization-independent
+          H2-approximation,
+          Proceedings of the 51st IEEE Conference on Decision and
+          Control, 2012.
+
+.. [BKS11] P. Benner, M. Köhler, J. Saak, Sparse-Dense Sylvester
+           Equations in :math:`\mathcal{H}_2`-Model Order
+           Reduction,
+           Max Planck Institute Magdeburg Preprint, available
+           from http://www.mpi-magdeburg.mpg.de/preprints/,
+           2011.
+
+.. [BEOR14] A. Buhr, C. Engwer, M. Ohlberger, S. Rave, A Numerically Stable A
+            Posteriori Error Estimator for Reduced Basis Approximations of Elliptic
+            Equations, Proceedings of the 11th World Congress on Computational
+            Mechanics, 2014.
+
+.. [CLVV06] Y. Chahlaoui, D. Lemonnier, A. Vandendorpe, P. Van
+            Dooren,
+            Second-order balanced truncation,
+            Linear Algebra and its Applications, 2006, 415(2–3),
+            373-384
+
+.. [GP05]   M. A. Grepl, A. T. Patera, A Posteriori Error Bounds For Reduced-Basis
+            Approximations Of Parametrized Parabolic Partial Differential Equations,
+            M2AN 39(1), 157-181, 2005.
+
+.. [GAB08] S. Gugercin, A. C. Antoulas, C. A. Beattie,
+           :math:`\mathcal{H}_2` model reduction for large-scale
+           linear dynamical systems,
+           SIAM Journal on Matrix Analysis and Applications, 30(2),
+           609-638, 2008.
+
+.. [HDO11] Haasdonk, B.; Dihlmann, M. & Ohlberger, M.,
+           A training set and multiple bases generation approach for
+           parameterized model reduction based on adaptive grids in
+           parameter space,
+           Math. Comput. Model. Dyn. Syst., 2011, 17, 423-442
+
+.. [HO08]  B. Haasdonk, M. Ohlberger, Reduced basis method for finite volume
+           approximations of parametrized evolution equations,
+           M2AN 42(2), 277-302, 2008.
+
+.. [PK16]  P. Kürschner,
+           Efficient Low-Rank Solution of Large-Scale Matrix Equations,
+           Shaker Verlag Aachen, available from
+           http://pubman.mpdl.mpg.de/pubman/, 2016.
+
+.. [MS96] D. G. Meyer and S. Srinivasan,
+          Balancing and model reduction for second-order form linear
+          systems,
+          IEEE Trans. Automat. Control, 1996, 41, 1632–1644
+
+.. [MG91]  D. Mustafa, K. Glover, Controller Reduction by
+           :math:`\mathcal{H}_\infty`-Balanced Truncation,
+           IEEE Transactions on Automatic Control, 36(6), 668-682,
+           1991.
+
+.. [OJ88]  P. C. Opdenacker, E. A. Jonckheere, A Contraction Mapping
+           Preserving Balanced Reduction Scheme and Its Infinity Norm
+           Error Bounds,
+           IEEE Transactions on Circuits and Systems, 35(2), 184-189,
+           1988.
+
+.. [RS08] T. Reis and T. Stykel,
+          Balanced truncation model reduction of second-order
+          systems,
+          Math. Comput. Model. Dyn. Syst., 2008, 14(5), 391-406
+
+.. [W12] S. Wyatt,
+         Issues in Interpolatory Model Reduction: Inexact Solves,
+         Second Order Systems and DAEs,
+         PhD thesis, Virginia Tech, 2012
+
+.. [XZ11] Y. Xu and T. Zeng, Optimal :math:`\mathcal{H}_2` Model
+          Reduction for Large Scale MIMO Systems via Tangential
+          Interpolation,
+          International Journal of Numerical Analysis and
+          Modeling, vol. 8, no. 1, pp. 174-188, 2011
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -17,6 +17,7 @@ computing stack are provided for getting started quickly.
     technical_overview
     environment
     release_notes
+    bibliography
 
 API Documentation
 *****************

diff --git a/src/pymor/algorithms/adaptivegreedy.py b/src/pymor/algorithms/adaptivegreedy.py
@@ -33,12 +33,6 @@ def adaptive_greedy(d, reductor, parameter_space=None,
     set and the validation set is larger than `rho`, the training set
     is refined using standard grid refinement techniques.
 
-    .. [HDO11] Haasdonk, B.; Dihlmann, M. & Ohlberger, M.,
-               A training set and multiple bases generation approach for
-               parameterized model reduction based on adaptive grids in
-               parameter space,
-               Math. Comput. Model. Dyn. Syst., 2011, 17, 423-442
-
     Parameters
     ----------
     d

diff --git a/src/pymor/algorithms/lyapunov.py b/src/pymor/algorithms/lyapunov.py
@@ -92,11 +92,6 @@ def lradi(A, E, B, trans=False, options=None):
     """Find a factor of the solution of a Lyapunov equation using the
     low-rank ADI iteration as described in Algorithm 4.3 in [PK16]_.
 
-    .. [PK16]  P. Kürschner,
-               Efficient Low-Rank Solution of Large-Scale Matrix Equations,
-               Shaker Verlag Aachen, available from
-               http://pubman.mpdl.mpg.de/pubman/, 2016.
-
     Parameters
     ----------
     A

diff --git a/src/pymor/reductors/bt.py b/src/pymor/reductors/bt.py
@@ -122,10 +122,6 @@ class BTReductor(GenericBTReductor):
 
     See Section 7.3 in [A05]_.
 
-    .. [A05] A. C. Antoulas, Approximation of Large-Scale Dynamical
-             Systems,
-             SIAM, 2005.
-
     Parameters
     ----------
     d
@@ -144,11 +140,6 @@ class LQGBTReductor(GenericBTReductor):
 
     See Section 3 in [MG91]_.
 
-    .. [MG91] D. Mustafa, K. Glover, Controller Reduction by
-              :math:`\mathcal{H}_\infty`-Balanced Truncation,
-              IEEE Transactions on Automatic Control, 36(6), 668-682,
-              1991.
-
     Parameters
     ----------
     d
@@ -200,12 +191,6 @@ class BRBTReductor(GenericBTReductor):
 
     See [A05]_ (Section 7.5.3) and [OJ88]_.
 
-    .. [OJ88] P. C. Opdenacker, E. A. Jonckheere, A Contraction Mapping
-              Preserving Balanced Reduction Scheme and Its Infinity Norm
-              Error Bounds,
-              IEEE Transactions on Circuits and Systems, 35(2), 184-189,
-              1988.
-
     Parameters
     ----------
     d

diff --git a/src/pymor/reductors/coercive.py b/src/pymor/reductors/coercive.py
@@ -21,11 +21,6 @@ class CoerciveRBReductor(GenericRBReductor):
     :class:`~pymor.reductors.residual.ResidualReductor` for improved numerical stability
     [BEOR14]_.
 
-    .. [BEOR14] A. Buhr, C. Engwer, M. Ohlberger, S. Rave, A Numerically Stable A
-                Posteriori Error Estimator for Reduced Basis Approximations of Elliptic
-                Equations, Proceedings of the 11th World Congress on Computational
-                Mechanics, 2014.
-
     Parameters
     ----------
     d

diff --git a/src/pymor/reductors/h2.py b/src/pymor/reductors/h2.py
@@ -33,18 +33,6 @@ def reduce(self, r, sigma=None, b=None, c=None, rd0=None, tol=1e-4, maxit=100, n
 
         See [GAB08]_ (Algorithm 4.1) and [ABG10]_ (Algorithm 1).
 
-        .. [GAB08] S. Gugercin, A. C. Antoulas, C. A. Beattie,
-                :math:`\mathcal{H}_2` model reduction for large-scale
-                linear dynamical systems,
-                SIAM Journal on Matrix Analysis and Applications, 30(2),
-                609-638, 2008.
-
-        .. [ABG10] A. C. Antoulas, C. A. Beattie, S. Gugercin,
-                Interpolatory model reduction of large-scale dynamical
-                systems,
-                Efficient Modeling and Control of Large-Scale Systems,
-                Springer-Verlag, 2010.
-
         Parameters
         ----------
         r
@@ -255,19 +243,6 @@ def reduce(self, rd0, tol=1e-4, maxit=100, num_prev=1, projection='orth', conv_c
         eigenvalue decomposition. Therefore, TSIA might behave better
         for non-normal reduced matrices.
 
-        .. [XZ11] Y. Xu and T. Zeng, Optimal :math:`\mathcal{H}_2` Model
-                  Reduction for Large Scale MIMO Systems via Tangential
-                  Interpolation,
-                  International Journal of Numerical Analysis and
-                  Modeling, vol. 8, no. 1, pp. 174-188, 2011
-
-        .. [BKS11] P. Benner, M. Köhler, J. Saak, Sparse-Dense Sylvester
-                   Equations in :math:`\mathcal{H}_2`-Model Order
-                   Reduction,
-                   Max Planck Institute Magdeburg Preprint, available
-                   from http://www.mpi-magdeburg.mpg.de/preprints/,
-                   2011.
-
         Parameters
         ----------
         rd0
@@ -388,11 +363,6 @@ class TF_IRKAReductor(BasicInterface):
 
     See [BG12]_.
 
-    .. [BG12] C. A. Beattie, S. Gugercin, Realization-independent
-              H2-approximation,
-              Proceedings of the 51st IEEE Conference on Decision and
-              Control, 2012.
-
     Parameters
     ----------
     d

diff --git a/src/pymor/reductors/interpolation.py b/src/pymor/reductors/interpolation.py
@@ -22,10 +22,6 @@ class GenericBHIReductor(GenericPGReductor):
     only up to the first derivative. Hence, interpolation points are
     assumed to be pairwise distinct.
 
-    .. [BG09] C. A. Beattie, S. Gugercin, Interpolatory projection
-              methods for structure-preserving model reduction,
-              Systems & Control Letters 58, 2009
-
     Parameters
     ----------
     d

diff --git a/src/pymor/reductors/parabolic.py b/src/pymor/reductors/parabolic.py
@@ -43,13 +43,6 @@ class ParabolicRBReductor(GenericRBReductor):
         energy product. If not, the projection of the initial values will be
         computed incorrectly.
 
-    .. [GP05]   M. A. Grepl, A. T. Patera, A Posteriori Error Bounds For Reduced-Basis
-                Approximations Of Parametrized Parabolic Partial Differential Equations,
-                M2AN 39(1), 157-181, 2005.
-    .. [HO08]   B. Haasdonk, M. Ohlberger, Reduced basis method for finite volume
-                approximations of parametrized evolution equations,
-                M2AN 42(2), 277-302, 2008.
-
     Parameters
     ----------
     d

diff --git a/src/pymor/reductors/sobt.py b/src/pymor/reductors/sobt.py
@@ -19,11 +19,6 @@ class GenericSOBTpvReductor(GenericPGReductor):
 
     See [RS08]_.
 
-    .. [RS08] T. Reis and T. Stykel,
-              Balanced truncation model reduction of second-order
-              systems,
-              Math. Comput. Model. Dyn. Syst., 2008, 14(5), 391-406
-
     Parameters
     ----------
     d
@@ -197,11 +192,6 @@ class SOBTfvReductor(GenericPGReductor):
 
     See [MS96]_.
 
-    .. [MS96] D. G. Meyer and S. Srinivasan,
-              Balancing and model reduction for second-order form linear
-              systems,
-              IEEE Trans. Automat. Control, 1996, 41, 1632–1644
-
     Parameters
     ----------
     d
@@ -278,12 +268,6 @@ class SOBTReductor(BasicInterface):
 
     See [CLVV06]_.
 
-    .. [CLVV06] Y. Chahlaoui, D. Lemonnier, A. Vandendorpe, P. Van
-                Dooren,
-                Second-order balanced truncation,
-                Linear Algebra and its Applications, 2006, 415(2–3),
-                373-384
-
     Parameters
     ----------
     d

diff --git a/src/pymor/reductors/sor_irka.py b/src/pymor/reductors/sor_irka.py
@@ -31,11 +31,6 @@ def reduce(self, r, sigma=None, b=None, c=None, rd0=None, tol=1e-4, maxit=100, n
         It uses IRKA as the intermediate reductor, to reduce from 2r poles to r.
         See Section 5.3.2 in [W12]_.
 
-        .. [W12] S. Wyatt,
-                 Issues in Interpolatory Model Reduction: Inexact Solves,
-                 Second Order Systems and DAEs,
-                 PhD thesis, Virginia Tech, 2012
-
         Parameters
         ----------
         r