diff --git a/README.md b/README.md
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@@ -63,11 +63,15 @@ The two definitive texts on HMC are [Neal (2011)](https://arxiv.org/pdf/1206.190
 SMC finds application in many areas, but dynamic (linear) models deserve a special mention. The seminal 1997 [book](https://link.springer.com/book/10.1007/b98971) by West and Harrison remains the _de facto_ text on the subject. 
 
 ## Optmisation
-#### The EM algortithm 
+#### The EM algorithm 
 - This elementary [tutorial](https://zhwa.github.io/tutorial-of-em-algorithm.html)  is simple but effective.
 - The book [The EM algorithm and Extensions](https://books.google.com.br/books?hl=en&lr=&id=NBawzaWoWa8C&oi=fnd&pg=PR3&dq=The+EM+algorithm+and+Extensions&ots=tp68LOYAvP&sig=iCEMt5YUIMToTSESxLctWcob8VM#v=onepage&q=The%20EM%20algorithm%20and%20Extensions&f=false) is a well-cited resource.
 - [Monte Carlo EM](https://github.com/bob-carpenter/case-studies/blob/master/monte-carlo-em/mcem.pdf) by Bob Carpenter (Columbia).
 
+#### Simulated Annealing
+- The original 1983 [paper](https://www.science.org/doi/10.1126/science.220.4598.671) in Science [open link](http://wexler.free.fr/library/files/kirkpatrick%20(1983)%20optimization%20by%20simulated%20annealing.pdf) by Kirpatrick et al is a great read.
+- [These](https://youtu.be/NPE3zncXA5s) visualisations of the traveling salesman problem might prove useful. 
+
 ## Miscellanea
 
 - In [these](https://terrytao.wordpress.com/2010/01/03/254a-notes-1-concentration-of-measure/) notes, [Terence Tao](https://en.wikipedia.org/wiki/Terence_Tao) gives insights into **concentration of measure**, which is the reason why integrating with respect to a probability measure in high-dimensional spaces is _hard_.