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I am a novice to algorithms and data structures. I recently started participating on codeforces and spoj etc.for solving questions i need to study dp,greedy algorithms,graph algorithms,data structures.what should be my strategy for studying or rather sequence and what data structures i need to know for competitive programming?
All in all there shouldnt be an "optimal" sequence. Its all about understanding the topic. Since not two people can learn with the same speed, there shouldnt be something like an "optimal" sequence. But its good to learn basic approaches of each topic.
There are alot Tutorials out there, which explains the most fundamental thing in any topic. E.g. Youtube covers most graph problems. Even DP and so can be found there. Especially on Topcoder Tutorials there is alot you can learn.
On the other hand you will learn nearly nothing, if u dont have to think for yourself. So solving such puzzles is a must. I would recommend this site (especially for dp). Just check the "problem set" link on the site and look for dynamic programming.
I recommend you this book: Competitive Progamming, by Halim. Is very complete, and newbie-compatible.
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I'm mainly a selftought I started studying computer science half a year ago.
Before studying, I genneraly just tried to think of an algorithm to do the tasks I needed to be done - with sorting algorithms for example this was quite easy (they might not have been super efficient, but they did good enough). But sometimes I really struggled to come up with algorithms for quite typical problems:
One example for this is triangulation.
The problem is: I didn't know it was called like that. Quite a few hours later I finally found out about delauney triangulation, and all my problems where immediately solved.
Also in computer science I'm learning about a lot of specific algorithms, but my question is:
Is there a way to learn about all these "important" algorithms, maybe by type or something simular? Or is the only good way to keep studying and hope they come my way at some point?
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I am in a quest of understanding majority of important algorithm that SO community has used in read world applciations. I know a ready list can be extracted from wiki page. But, i am interested only those algorithm or problem that community has faced either in their projects or asked in interviews. Few lines on that algorithm will also be helpful.
I am looking beyond the generic algorithm D&C, DP, Greedy...
If you are interesting about optimization problems which can be used any computer applications such as network and socket programming these could be useful for you;
NearbyNeighbour
Munkres
Hungarian
BruteForce
Min&Max Finding Algorithms
Ant and Bee Colonies Algorithms
General Genetic Algorithm etc.
I totally advice you to search all aboves but genetic algorithms and ant colonies algorithm are asked many interviewers.
I hope that helps.
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I'm currently working on a recommendation system that uses collaborative filtering. And now I'm researching for a good/efficient algorithm that is geared towards movie recommendation. I'm confused because there are many algorithm like the Pearson Correlation Coefficient. And so I don't know what to use/implement.
Can you give me a good/efficient algorithm? or a site that gives a good example or simulation to the algorithm?
Thanks for the help!
Give this paper about the netflix prize a read Netflix Prize. Usually the 'state of the art' is some variant of matrix factorisation such as OrdRec. Check out the Funk Blog FunkSVD which will give you a nice simple explanation about implementing the starts of a matrix factorization technique for CF.
Matrix factorisation (An example in Python) is a good starting point. Furthermore, I'd recommend Ed Chen's Blog and The Mining of Massive Datasets as good introductions to the variety of methods used to solve this type of problem. The interesting thing for me having worked with this type of data is the amount of sparseness, there are of course practical limits, papers by Emmanual Candes seem to shed light on this area, excellent advanced work.
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I'm good at algorithms but not as good as converting real-time problems and learning them throughly to make it as an algorithm. I would like to know if there is any book/paper that teaches or makes you demystify the situation and formulate it as an algorithm. (Its much like training your mental ability to break the situation and comeup with algorithm in a crisp.)
Showing some of the ways to approach these kinda problems. and any easy learning links/material would help me a lot.
Note: I know SO doesnt allow to ask for the opinion or something vague (I dont mind my Q being downgraded). But I am asking some concrete problem and hope can get some nice info from some of the great minds here.
The word that fits better as a direct answer is "experience". There exists no magical formula to convert a real time problem into some algorithms that solve it. As an analogy, there exist no predefined patterns on how to solve a mathematical problem. It is a mind's task to express the solution, based on some fundamental knowledge and on experience that is accumulated though constant learning.
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This may not be question of programming and people are open to close.
Does anyone has list of questions/problems to solve which helps to improve algorithms skills
may be for interview purpose.
A good option is Project Euler.
In its own description:
Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems.
The motivation for starting Project Euler, and its continuation, is to provide a platform for the inquiring mind to delve into unfamiliar areas and learn new concepts in a fun and recreational context.
This seems like a perfect match...
Try TopCoder. They have held hundreds of algorithm competitions. Their archive contains thousands of problems for practice, including editorials describing the solutions. You can also view other people's submitted source code for the problems.
This will certainly sharpen your algorithm and problem solving skills, which should make you better prepared for algorithmic interview questions.
Fizz-Buzz?