Finish rework of citations.

JuliaManifolds · Jul 15, 2023 · 96249e4 · 96249e4
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diff --git a/_quarto.yml b/_quarto.yml
diff --git a/docs/src/references.bib b/docs/src/references.bib
@@ -113,7 +113,7 @@ @article{BergmannGousenbourger:2018
     NOTE         = {arXiv: [1807.10090](https://arxiv.org/abs/1807.10090)}
 }
 
-@article{BergmannHerzog:2019:1,
+@article{BergmannHerzog:2019,
     AUTHOR       = {Bergmann, Ronny and Herzog, Roland},
     DOI          = {10.1137/18M1181602},
     JOURNAL      = {SIAM Journal on Optimization},
@@ -354,7 +354,7 @@ @article{HestenesStiefel:1952
     VOLUME    = {49},
     YEAR      = {1952}
 }
-@thesis{Huang:2014,
+@phdthesis{Huang:2014,
     AUTHOR    = {Huang, W.},
     SCHOOL    = {Flordia State University},
     TITLE     = {Optimization algorithms on Riemannian manifolds with applications},

diff --git a/docs/src/tutorials/GeodesicRegression.md b/docs/src/tutorials/GeodesicRegression.md
@@ -34,7 +34,7 @@ highlighted = 4;
 ## Time Labeled Data
 
 If for each data item $d_i$ we are also given a time point $t_i\in\mathbb R$, which are pairwise different,
-then we can use the least squares error to state the objetive function as (Fletcher, 2013)
+then we can use the least squares error to state the objetive function as [Fletcher, IJCV, 2013](@cite Fletcher:2013).
 
 ``` math
 F(p,X) = \frac{1}{2}\sum_{i=1}^n d_{\mathcal M}^2(γ_{p,X}(t_i), d_i),
@@ -71,7 +71,7 @@ Due to linearity, the gradient of $F(p,X)$ is the sum of the single gradients of
 ```
 
 which can be computed using a chain rule of the squared distance and the exponential map,
-see for example (Bergmann and Gousenbourger, 2018) for details or Equations (7) and (8) of (Fletcher, 2013):
+see for example [Bergmann, Gousenbourger, Front. Appl. Math. Stat., 2018](@cite BergmannGousenbourger:2018) for details or Equations (7) and (8) of [Fletcher, IJCV, 2013](@cite Fletcher:2013):
 ““”
 
 ``` julia
@@ -514,6 +514,8 @@ Note that the geodesics from the data to the regression geodesic meet at a nearl
 
 ## Literature
 
-Bergmann, R. and Gousenbourger, P.-Y. (2018) “A variational model for data fitting on manifolds by minimizing the acceleration of a Bézier curve,” *Frontiers in Applied Mathematics and Statistics*, 4. Available at: <https://doi.org/10.3389/fams.2018.00059>.
+```@bibliography
+Pages = ["tutorials/GeodesicRegression.md"]
+Canonical=false
+```
 
-Fletcher, P.T. (2013) “Geodesic regression and the theory of least squares on Riemannian manifolds,” *International Journal of Computer Vision*, 105(2), pp. 171–185. Available at: <https://doi.org/10.1007/s11263-012-0591-y>.
diff --git a/docs/src/tutorials/InplaceGradient.md b/docs/src/tutorials/InplaceGradient.md
@@ -57,14 +57,14 @@ We can also benchmark this as
 @benchmark gradient_descent($M, $f, $grad_f, $p0; stopping_criterion=$sc)
 ```
 
-    BenchmarkTools.Trial: 102 samples with 1 evaluation.
-     Range (min … max):  47.810 ms …  53.557 ms  ┊ GC (min … max): 5.09% … 6.53%
-     Time  (median):     48.820 ms               ┊ GC (median):    5.34%
-     Time  (mean ± σ):   49.060 ms ± 818.642 μs  ┊ GC (mean ± σ):  5.77% ± 0.64%
+    BenchmarkTools.Trial: 100 samples with 1 evaluation.
+     Range (min … max):  48.285 ms … 56.649 ms  ┊ GC (min … max): 4.84% … 6.96%
+     Time  (median):     49.552 ms              ┊ GC (median):    5.41%
+     Time  (mean ± σ):   50.151 ms ±  1.731 ms  ┊ GC (mean ± σ):  5.56% ± 0.64%
 
-                ▅▅█      ▃▃                                         
-      ▄▃▁▅▄▁▅▃▃▄███▅▅▇▃▁▆███▁▃▅▁▃▁▁▁▁▁▁▁▁▁▁▁▃▃▃▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃ ▃
-      47.8 ms         Histogram: frequency by time         52.4 ms <
+       ▂▃ █▃▃▆    ▂                                                
+      ▅████████▅█▇█▄▅▇▁▅█▅▇▄▇▅▁▅▄▄▄▁▄▁▁▁▄▄▁▁▁▁▁▁▄▁▁▁▁▁▁▄▁▄▁▁▁▁▁▁▄ ▄
+      48.3 ms         Histogram: frequency by time        56.6 ms <
 
      Memory estimate: 194.10 MiB, allocs estimate: 655347.
 
@@ -115,14 +115,14 @@ We can again benchmark this
 ) setup = (m2 = deepcopy($p0))
 ```
 
-    BenchmarkTools.Trial: 179 samples with 1 evaluation.
-     Range (min … max):  27.027 ms …  31.367 ms  ┊ GC (min … max): 0.00% … 11.00%
-     Time  (median):     27.712 ms               ┊ GC (median):    0.00%
-     Time  (mean ± σ):   27.939 ms ± 779.920 μs  ┊ GC (mean ± σ):  0.84% ±  2.56%
+    BenchmarkTools.Trial: 176 samples with 1 evaluation.
+     Range (min … max):  27.419 ms … 34.154 ms  ┊ GC (min … max): 0.00% … 0.00%
+     Time  (median):     28.001 ms              ┊ GC (median):    0.00%
+     Time  (mean ± σ):   28.412 ms ±  1.079 ms  ┊ GC (mean ± σ):  0.73% ± 2.24%
 
-             ▄▃▆█▇▄▇                                                
-      ▅▁▁▅▅▅▇████████▅▇▆▁▅▁▁▅▁▁▁▅▁▁▁▁▁▁▁▁▁▁▁▁▅▁▁▁▅▁▁▁▁▁▅▆▁▅▅▁▁▁▇▁▇ ▅
-      27 ms         Histogram: log(frequency) by time      30.7 ms <
+        ▁▅▇█▅▂▄ ▁                                                  
+      ▄▁███████▆█▇█▄▆▃▃▃▃▁▁▃▁▁▃▁▃▃▁▄▁▁▃▃▁▁▄▁▁▃▅▃▃▃▁▃▃▁▁▁▁▁▁▁▁▃▁▁▃ ▃
+      27.4 ms         Histogram: frequency by time        31.9 ms <
 
      Memory estimate: 3.76 MiB, allocs estimate: 5949.
 

diff --git a/tutorials/ConstrainedOptimization.qmd b/tutorials/ConstrainedOptimization.qmd
@@ -252,5 +252,9 @@ maximum(g(M, w1))
 
 ## Literature
 
-::: {#refs}
-:::
+````{=commonmark}
+```@bibliography
+Pages = ["tutorials/ConstrainedOptimization.md"]
+Canonical=false
+```
+````
diff --git a/tutorials/GeodesicRegression.qmd b/tutorials/GeodesicRegression.qmd
@@ -510,5 +510,9 @@ Note that the geodesics from the data to the regression geodesic meet at a nearl
 
 ## Literature
 
-::: {#refs}
-:::
+````{=commonmark}
+```@bibliography
+Pages = ["tutorials/GeodesicRegression.md"]
+Canonical=false
+```
+````