<p>The Design of Approximation Algorithms by Williamson &amp; Shmoys (<a href="http://www.designofapproxalgs.com/" rel="nofollow">http://www.designofapproxalgs.com/</a>) is a great book for many approximation methods such as greedy algorithms, semidefinite programming, etc. Also, it covers some topics within complexity that are closely related to approximation algorithms (inapproximability, Unique Games-based hardness of MAX-CUT).</p>

CC BY-SA 3.0
2013-11-05T03:35:02.660Z
2013-11-06T02:25:35.517Z
2013-11-06T02:25:35.517Z
rahulmehta95
<p>You may find of interest the following recent handbooks.
The range of topics covered goes well beyond CLRS, and the material is well suited for graduate and Ph.D. students, even though you may choose a few selected topics for advanced undergraduate students.   </p>

<p><a href="http://rads.stackoverflow.com/amzn/click/1584888180">Algorithms and Theory of
Computation Handbook Second Edition (Special Topics and Techniques)</a></p>

<p><a href="http://rads.stackoverflow.com/amzn/click/0470044926">Handbook of Applied Algorithms Solving Scientific, Engineering and Practical problems</a></p>

<p><a href="http://rads.stackoverflow.com/amzn/click/1584885505">Handbook of Approximation Algorithms and Metaheuristics</a>
￼￼￼￼￼</p>

CC BY-SA 3.0
2013-11-05T18:03:36.640Z
review & table of contents of the 1st ref [Atallah/Blanton](http://www.dcs.warwick.ac.uk/~nikos/downloads/handbookreview.pdf)
vzn
CC BY-SA 3.0
2013-11-05T07:00:16.717Z
2013-11-05T07:28:33.373Z
2013-11-05T07:28:33.373Z
Massimo Cafaro
<p>Have look at the <a href="http://rads.stackoverflow.com/amzn/click/0387307702" rel="nofollow">Encyclopedia of Algorithms</a> by Kao (Editor). It contains over 500 entries and many of them contain advanced algorithms.</p>

CC BY-SA 3.0
2013-11-05T15:50:38.363Z
[table of contents](http://link.springer.com/referencework/10.1007%2F978-0-387-30162-4)
vzn
CC BY-SA 3.0
2013-11-05T14:23:22.310Z
2013-11-05T14:23:22.310Z
Mohammad Al-Turkistany
<p>I rather liked "Algorithmics for Hard Problems" by Juraj Hromkovic</p>

CC BY-SA 3.0
2013-11-04T09:58:04.470Z
2013-11-04T09:58:04.470Z
n00b
<p><strong>Computational Geometry:</strong> Mark de Berg, Marc van Kreveld, Mark Overmars, and Otfried Cheong. Computational Geometry: Algorithms and Applications; <a href="http://www.cs.umd.edu/~mount/754/Lects/754lects.pdf" rel="nofollow">David Mount's Course Notes</a>.</p>

<p><strong>Randomized Algorithms:</strong> Motwani and Raghavan. Randomized Algorithms; <a href="http://www.cs.yale.edu/homes/aspnes/classes/469/notes.pdf" rel="nofollow">Excellent Notes by James Aspnes</a>; Mitzenmacher and Upfal. Probability and Computing.</p>

<p><strong>Network Flows:</strong> Ahuja, Magnanti, and Orlin. Network Flows. </p>

<p><strong>Approximation Algorithms:</strong> Dorit Hochbaum. Approximation Algorithms for NP-Hard Problems.
￼￼￼￼￼</p>

CC BY-SA 3.0
2013-11-05T19:40:32.227Z
Since there might not be a single "Advanced Algorithms Handbook", a community wiki answer along these lines (by advanced algorithms topic) would be nice.
Huck Bennett
CC BY-SA 3.0
2013-11-10T18:57:39.427Z
+1 for Ahuja, et. al. Great book -- unfortunately, it doesn't cover many of the recent results such as Orlin's $O(mn)$-time algorithm and Madry's algorithm that solves Laplacians for electrical flows.
rahulmehta95
CC BY-SA 3.0
2013-11-05T15:59:10.900Z
2013-11-05T21:48:25.357Z
2013-11-05T21:48:25.357Z
Jinx
<p>this ref is recommended by Lance Fortnow (on his blog).</p>

<p><a href="http://rads.stackoverflow.com/amzn/click/0262720140" rel="nofollow">Jan van Leeuwen, editor. Handbook of Theoretical Computer Science, volume A: Algorithms
and Complexity. MIT Press, 1994.</a></p>

CC BY-SA 3.0
2013-11-05T18:22:17.197Z
2013-11-05T18:22:17.197Z
vzn
<p>not exactly what is desired yet similar to your example, consider <a href="http://www.ccs.neu.edu/home/viola/classes/gems-08/index.html" rel="nofollow">CS G399: Gems of Theoretical Computer Science; Spring 2009</a> lecture notes by Viola. its more a proof-centric perspective however most are essentially advanced algorithms in key frontier research areas. (also note lower bounds proofs can be regarded as compression algorithms.)</p>

<blockquote>
  <p>This course covers some of the most exciting and recent progress in theoretical computer science. It presents state-of-the-art results on active research areas, and teaches related proof techniques. A tentative list of topics includes: </p>
  
  <ul>
  <li>Lower bounds for constant-depth circuits. </li>
  <li>The Nisan-Wigderson pseudorandom generator. </li>
  <li>Cryptography in constant parallel time. </li>
  <li>The complexity of Nash equilibria. </li>
  <li>Undirected connectivity in logarithmic space (SL = L). </li>
  <li>Communication complexity. </li>
  <li>Primes is in P. </li>
  <li>Fast matrix multiplication. </li>
  </ul>
</blockquote>

CC BY-SA 3.0
2013-11-06T03:43:40.923Z
nice course, but much wider than what the OP was asking
Alessandro Cosentino
CC BY-SA 3.0
2013-11-04T16:18:20.497Z
2013-11-04T16:18:20.497Z
vzn
<p>I am looking for resources (preferably a handbook) on advanced topics in algorithms (topics beyond what is covered in algorithms textbooks like CLRS and DPV). </p>

<p>The type of material that can be used for teaching a topics in algorithms course like 
Erik Demaine and David Karger's <a href="http://courses.csail.mit.edu/6.854/03/">Advanced Algorithms course</a>.</p>

<p>Resources that would give an overview of the field (like a handbook) are preferable, 
but more focused resources like Vijay Vazirani's "Approximation Algorithms" book are also fine.</p>

CC BY-SA 3.0
2013-11-03T22:42:51.747Z
This is similar to my previous question about data structures: [handbook of advanced data strctures](http://cstheory.stackexchange.com/q/12824/). I would like to use them as pointers for my students to learn about more advanced topics in algorithms. Resources which are available online for students are preferable.
Kaveh
CC BY-SA 3.0
2013-11-06T05:05:12.943Z
Search [the MIT archives](http://dspace.mit.edu/).
Tommy
CC BY-SA 3.0
2013-12-09T17:26:24.070Z
Johan Håstad (also) has lecture notes on advanced algorithms: http://www.nada.kth.se/~johanh/algnotes.pdf
Huck Bennett
<ds.algorithms><reference-request><survey><books>
Handbook of advanced algorithms
Kaveh
# Handbook of advanced algorithms

(Asked by: username_0 on 2013-11-03)

I am looking for resources (preferably a handbook) on advanced topics in algorithms (topics beyond what is covered in algorithms textbooks like CLRS and DPV).

The type of material that can be used for teaching a topics in algorithms course like
Erik Demaine and David Karger's [Advanced Algorithms course](http://courses.csail.mit.edu/6.854/03/).

Resources that would give an overview of the field (like a handbook) are preferable,
but more focused resources like Vijay Vazirani's "Approximation Algorithms" book are also fine.

**Tags:** ds.algorithms, reference-request, survey, books
> **Comments:**
> **username_0**: This is similar to my previous question about data structures: [handbook of advanced data strctures](http://cstheory.stackexchange.com/q/12824/). I would like to use them as pointers for my students to learn about more advanced topics in algorithms. Resources which are available online for students are preferable.  
> **username_1**: Search [the MIT archives](http://dspace.mit.edu/).  
> **username_2**: Johan Håstad (also) has lecture notes on advanced algorithms: http://www.nada.kth.se/~johanh/algnotes.pdf  

---

## 7 answers

### Answer by username_3 on 2013-11-05. Score: 6

The Design of Approximation Algorithms by Williamson & Shmoys (<http://www.designofapproxalgs.com/>) is a great book for many approximation methods such as greedy algorithms, semidefinite programming, etc. Also, it covers some topics within complexity that are closely related to approximation algorithms (inapproximability, Unique Games-based hardness of MAX-CUT).


---

### Answer by username_4 on 2013-11-05. Score: 5

You may find of interest the following recent handbooks.
The range of topics covered goes well beyond CLRS, and the material is well suited for graduate and Ph.D. students, even though you may choose a few selected topics for advanced undergraduate students.

[Algorithms and Theory of
Computation Handbook Second Edition (Special Topics and Techniques)](http://rads.stackoverflow.com/amzn/click/1584888180)

[Handbook of Applied Algorithms Solving Scientific, Engineering and Practical problems](http://rads.stackoverflow.com/amzn/click/0470044926)

[Handbook of Approximation Algorithms and Metaheuristics](http://rads.stackoverflow.com/amzn/click/1584885505)
￼￼￼￼￼

> **Comments:**
> **username_5**: review & table of contents of the 1st ref [Atallah/Blanton](http://www.dcs.warwick.ac.uk/~nikos/downloads/handbookreview.pdf)  

---

### Answer by username_6 on 2013-11-05. Score: 4

Have look at the [Encyclopedia of Algorithms](http://rads.stackoverflow.com/amzn/click/0387307702) by Kao (Editor). It contains over 500 entries and many of them contain advanced algorithms.

> **Comments:**
> **username_5**: [table of contents](http://link.springer.com/referencework/10.1007%2F978-0-387-30162-4)  

---

### Answer by username_7 on 2013-11-04. Score: 4

I rather liked "Algorithmics for Hard Problems" by Juraj Hromkovic


---

### Answer by username_8 on 2013-11-05. Score: 4

**Computational Geometry:** Mark de Berg, Marc van Kreveld, Mark Overmars, and Otfried Cheong. Computational Geometry: Algorithms and Applications; [David Mount's Course Notes](http://www.cs.umd.edu/~mount/754/Lects/754lects.pdf).

**Randomized Algorithms:** Motwani and Raghavan. Randomized Algorithms; [Excellent Notes by James Aspnes](http://www.cs.yale.edu/homes/aspnes/classes/469/notes.pdf); Mitzenmacher and Upfal. Probability and Computing.

**Network Flows:** Ahuja, Magnanti, and Orlin. Network Flows.

**Approximation Algorithms:** Dorit Hochbaum. Approximation Algorithms for NP-Hard Problems.
￼￼￼￼￼

> **Comments:**
> **username_2**: Since there might not be a single "Advanced Algorithms Handbook", a community wiki answer along these lines (by advanced algorithms topic) would be nice.  
> **username_3**: +1 for Ahuja, et. al. Great book -- unfortunately, it doesn't cover many of the recent results such as Orlin's $O(mn)$-time algorithm and Madry's algorithm that solves Laplacians for electrical flows.  

---

### Answer by username_5 on 2013-11-05. Score: 0

this ref is recommended by Lance Fortnow (on his blog).

[Jan van Leeuwen, editor. Handbook of Theoretical Computer Science, volume A: Algorithms
and Complexity. MIT Press, 1994.](http://rads.stackoverflow.com/amzn/click/0262720140)


---

### Answer by username_5 on 2013-11-04. Score: 0

not exactly what is desired yet similar to your example, consider [CS G399: Gems of Theoretical Computer Science; Spring 2009](http://www.ccs.neu.edu/home/viola/classes/gems-08/index.html) lecture notes by Viola. its more a proof-centric perspective however most are essentially advanced algorithms in key frontier research areas. (also note lower bounds proofs can be regarded as compression algorithms.)

> This course covers some of the most exciting and recent progress in theoretical computer science. It presents state-of-the-art results on active research areas, and teaches related proof techniques. A tentative list of topics includes:
>
> * Lower bounds for constant-depth circuits.
> * The Nisan-Wigderson pseudorandom generator.
> * Cryptography in constant parallel time.
> * The complexity of Nash equilibria.
> * Undirected connectivity in logarithmic space (SL = L).
> * Communication complexity.
> * Primes is in P.
> * Fast matrix multiplication.

> **Comments:**
> **username_9**: nice course, but much wider than what the OP was asking  

---

