[Algorithm] 如何正确撸<算法导论>CLRS

其实算法本身不难，第一遍可以只看伪代码和算法思路。如果想进一步理解的话，第三章那些标记法是非常重要的，就算要花费大量时间才能理解，也不要马马虎虎略过。因为以后的每一章，讲完算法就是这样的分析，精通的话，很快就读完了。你所说的证明和推导大概也都是在第三章介绍了，可以回过头再认真看几遍。

至于课后题，比较难，我只做了前几章，如果要做完需要更多时间和精力。这可以通过之后做算法题来弥补，可以去leetcode等网站找一些经典的算法题做一做，加深理解。

Facebook的工程师写的攻略，介绍了用算法导论来应付面试应该读哪些，略过哪些，可以作为参考：

Chapter 1

Interesting read, but you can skip it.

Chapter 2

2.1 Insertion
Sort - To be honest you should probably know all major sorting
algorithms, not just insertion sort. It's just basic knowledge and you
never know when it can help.
2.2 Analysis of Algorithms - you can skip the small intro, but know the rest.
2.3 Designing
algorithms - contains merge sort and its analysis as well as an
overview of divide-and-conquer, very important stuff, so worth a read.

Chapter 3

All of it. You have to know big-O notation and time complexity analysis, period.

Chapter 4

4.1 Maximum
subarray problem - Can kind of be worth your time. There are better
solutions to this problem than divide and conquer but it's good practice
and the flow of logic may help develop how you think.
4.2 Strassen's
algorithm - I really love this algorithm and was astounded at how cool
it was the first time I saw it, but you can skip it for the interviews.
It won't come up.
4.3 Substitution method - you won't be using
this method in an interview, but you should know it since it's a basic
tool for finding the time complexity of a recursive algorithm.
4.4 Recurrence tree method - same as 4.3
4.5 Master
method - essential knowledge. You should know it and practice with it
and be able to use it in 3 seconds. This is the method you would use in
an interview if analyzing a recursive algorithm that fits the form.
4.6 Proof
of the master theorem - you can probably skip this, though it's good to
read at least once so that you understand what you're doing with the
master method.

Chapter 5

I've never read this chapter, to
be honest, but what I know is that you need a basic grasp of
probability in interviews because there's a good chance they may come
up. That said, as long as you know basic probability concepts and
practice on probability-related interview problems (there are such
problems with solution explanations in Elements of Programming Interviews,
the book I recommend for interview prep), you can probably skip this
chapter. From a cursory glance, it's more math than algorithms.

Chapter 6

6.1, 6.2, 6.3, 6.4, 6.5 - Heaps and heapsort. Check.

Chapter 7

7.1, 7.2, 7.3 - Quicksort
and its randomized version. Need-to-know concepts. I also recommend 7.4
(I was once asked in an interview to high-level-analyze a randomized
algorithm), though the probability you have to deal with something like
7.4 in an interview is pretty low, I'd guess.

Chapter 8

8.1 - Lower
bounds on sorting - Yes. Basic knowledge. May be asked in a Google
interview (though unlikely, I know of a case it happened in before).
8.2 - Counting sort - Need-to-know in detail. It comes up in disguised forms.
8.3 - Radix sort - Yup. It's an easy algorithm anyway.
8.4 - Bucket sort - can skip.

Chapter 9

9.1 - Small section, worth a read.
9.2 - Selection in expected linear time - Very important,
as it's not common knowledge like quicksort and yet it comes up often
in interviews. I had to code the entire thing in an interview once.
9.3 - Selection
in worst-case linear time - Can skip. Just know that it's possible in
worst-case linear time, because that might help somewhat.

Chapter 10

10.1 - Stacks and queues - basic knowledge, definitely very important.
10.2 - Linked lists - same as 10.1
10.3 - Implementing pointers and objects - If you use C++ or Java, skip this. Otherwise I'm not sure.
10.4 - Representing rooted trees - Small section, worth a quick read.

Chapter 11

For
hashing, I'd say the implementation isn't as important to know as, for
example, linked lists, but you should definitely have an idea about it
and most importantly know the (expected and worst-case) time
complexities of search/insert/delete etc. Also know that practically,
they're very important data structures and, also practically, the
expected time complexity is what matters in the real world.
11.1 - Direct addressing - Just understand the idea.
11.2 - Hash tables - important.
11.3 - Hash
functions - it's worth having an idea about them, but I wouldn't go too
in-depth here. Just know a couple examples of good and bad hash
functions (and why they are good/bad).
11.4 - Open addressing - Worth having an idea about, but unlikely to come up.
11.5 - Perfect hashing - skip.

Chapter 12

12.1 - What is a binary search tree? - Yep.
12.2 - Querying a BST - Yep. All of it.
12.3 - Insertion/Deletion - Same as 12.2
12.4 - Randomly built BSTs - just know Theorem 12.4 (expected height of random BST is O(lgn)) and an idea of why it's true.

Chapter 13

This one is easy. Know what a Red-Black tree is, and what its worst-case height/insert/delete/find are. Read 13.1 and 13.2,
and skip the rest. You will never be asked for RB-tree insert/delete
unless the interviewer is "doing it wrong", or if the interviewer wants
to see if you can re-derive the cases, in which case knowing them won't
help much anyway (and I doubt this would happen anyway). Also know that
RB-trees are pretty space-efficient and some C++ STL containers are
built as RB-trees usually (e.g. map/set).

Chapter 14

Might be worth skimming 14.2 just
to know that you can augment data structures and why it might be
helpful. Otherwise do one or two simple problems on augmenting data
structures and you're set here. I'd skip 14.1 and 14.3.

Chapter 15

DP! Must-know.
15.1 - Rod-cutting. Standard DP problem, must-know.
15.2 - Matrix-chain
multiplication - same as 15.1, though I don't particularly like the way
this section is written (it's rare for me to say that about CLRS).
15.3 - Elements
of DP - worth a read so that you understand DP properly, but I'd say
it's less important than knowing what DP is (via the chapter
introduction) and practicing on it (via the problems in this book and in
interview preparation books).
15.4 - LCS - same as 15.1
15.5 - Optimal binary search trees - I've never read this section, so I can't argue for its importance, but I did fine without it.

Chapter 16

You should definitely know what a greedy algorithm is, so read the introduction for this chapter.
16.1 - An activity selection problem - Haven't read this in detail, but I'd say check it out, if not in-depth.
16.2 - Elements of the greedy strategy - same as 16.1
16.3 - Huffman
codes - I'd say read the problem and the algorithm, but that's enough.
I've seen interview questions where the answer is Huffman coding (but
the question will come up in a 'disguised form', so it won't be
obvious.)
16.4 - Matroids and greedy methods - I've never read
this section, but I've done a lot of greedy problems during interview
prep and this stuff never came up, so I'd say this section is irrelevant
for the interview.
16.5 - Task-scheduling problem as a matroid - Same as 16.4.

Chapter 17

Okay,
you should definitely know what amortized analysis is, but I've never
read it from the book and I feel it's a sufficiently simple concept that
you can just Google it and check a few examples on what it is, or
understand it just by reading section 17.1. So:
17.1 - Aggregate analysis - read this, it explains the important stuff.
17.2, 17.3, 17.4 - Skip.

Chapter 18

You
should probably have an idea of what B-Trees (and B+ trees) are, I've
heard of cases where candidates were asked about them in a general sense
(high-level questions about what they are and why they're awesome). But
other than that I'd skip this chapter.

Chapter 19

Fibonacci heaps - nope.

Chapter 20

van Emde Boas Trees - double, triple, and quadruple nope.

Chapter 21

Disjoint sets
Update:
I originally recommended skipping this section, but on reconsideration,
I've noticed that it's actually more important than I originally
thought. Thus, I recommend reading sections 21.1 and 21.2, while skipping the rest.
Union-find
is somewhat important and I've seen at least one problem which uses it,
though that problem could also be solved using DFS and connected
components. That said, I also believe that it's not strictly necessary
because one can probably, for interview purposes, come up with a similar
enough structure easily to solve a problem which requires union-find,
without knowing the material in this chapter. However, I believe it's
worth a read so that if a problem comes up whose intended solution is a
union-find data structure, you don't spend time in an interview coming
up with it, and rather know from before, which can be a good advantage.
Still, I'd probably rank it as less important than most of the other
material in this list, and even less than other material that's not even
in CLRS (like tries, for example).

Okay, now graph algorithms. First read the introduction. Now, there's a lot to know here, so hang on.

Chapter 22

22.1 - Representations of graphs - Yes.
22.2 - BFS - Yes. After you do that, solve this problem: ACM-ICPC Live Archive - Kermit the Frog. The whole "state-space search using BFS" thing is an important concept that might be used to solve several interview problems.
22.3 - DFS - Yes.
22.4 - Topological sort - Yes.
22.5 - Strongly connected components - much less likely to come up than the above 4, but still possible, so: Yes.

Chapter 23

Minimum
spanning trees - probably the least important graph algorithm, other
than max flow (I mean for interview purposes, of course). I'd still say
you should read it because it's such a well-known problem, but
definitely give priority to the other things.
23.1 - Growing a MST - sort of, yes.
23.2 - Prim and Kruskal's algorithms - sort of, yes.

Chapter 24

Shortest path algorithms are important, though maybe less so than BFS/DFS.
Read
the introduction. You should, in general, read all introductions
anyway, but this one's important (and long), so it warranted a special
note.
24.1 Bellman-Ford - Know the algorithm and its proof of correctness.
24.2 Shortest paths in DAGs - definitely worth knowing, may come up, even more so than Bellman-Ford I'd say.
24.3 Dijkstra's
algorithm - Yes. Of course. I've seen this come up multiple times (with
slight variations), and I've even seen A* come up.
24.4 Difference constraints and shortest paths - Skip.

Chapter 25

Read the intro as well.
25.1 - Matrix multiplication -I'd
say skip. It might be possible for this to come up (very very slim
chance that it does though), but the chances are so low in my view that
it's probably not worth it. If you have some extra time, though, give it
a read.
25.2 - Floyd-Warshall - Yep, worth knowing the
algorithm and its time complexity and when it works (which is for all
weighted graphs, except ones with negative weight cycles). Its code is
something like 5 lines so there's no reason not to know it. The analysis
might be a bit overkill though.
25.3 - Johnson's algorithm - Skip.

Chapter 26

Maximum flow - I've never heard of this coming up in an interview and I can't imagine why it would, so skip.

Chapters 27+

Most
of this stuff is never going to come up, so it's easier for me to tell
you what to actually read than what not to read, so here are a few
selected topics from the Selected Topics in the book:

Chapter 31
Most
of what you should learn from this chapter you can learn from
practicing on interview problems from Elements of Programming Interviews
(and your time is better spent doing that), so I'd say skip it all
except Euclid's algorithm for the GCD, under section 31.2.

Chapter 32
32.1 - Naive method - just read it quickly.
32.2 - Rabin-Karp
- I'd say you should know this, the rolling hash concept is very
important and can be useful in many string- or search-related interview
problems.

Appendices

A - Summations
Know the important summations for time complexity analysis.

C - Counting and Probability
Give C.4
a read if you don't know the material, Bernoulli trials may come up in
problems (not explicitly, but you might use them, specifically for time
analysis of questions that involve probability/coin flips).

全部伪代码的Python实现

https://github.com/gycg/Algorithm