williamfiset / algorithms Goto Github PK
View Code? Open in Web Editor NEWA collection of algorithms and data structures
License: MIT License
A collection of algorithms and data structures
License: MIT License
Add quickselect algorithm. Quickselect is a selection algorithm to find the kth smallest element in an unordered list.
In the GaussianElimination.java file it is written that you should check for consistency and/or multiple solutions before trying to solve, however I don't think that the methods correctly do this for an arbitrary matrix. I think that the solve method should be run first in order to put the matrix into reduced row echelon form and from there the isInconsistent and hasMultipleSolutions can be used correctly. Does this look/sound right?
Add algorithm to find Eigen vectors/values along with a complete set of unit tests.
Use Kőnig's theorem.
Add algorithm to do the intersection of a line in general form (ax + by + cz + d = 0) with a sphere of radius r centered at some 3D coordinate (x, y, z).
This Kattis problem should be able to verify the functionality of the algorithm: https://open.kattis.com/problems/pop
Implement 2SAT algorithm
Here are some good problems to test the algorithm against:
https://open.kattis.com/problems/blackvienna
https://open.kattis.com/problems/pieceittogether
https://open.kattis.com/problems/palindromicdna
Still need to code up and document usage of how to use the following algorithms. Help is of course always appreciated :)
Add line segment intersection and test against:
https://open.kattis.com/problems/segmentintersection
Edit .travis.yml file to setup CI on travis-ci.org for more than just broken links
Once #30 is complete see if the performance can be improved by using a dynamic tree to obtain O(EVlogV) time complexity.
The traveling salesman problem can be solved in O(n!) with a brute force but can be reduced to (n22n) with DP.
See here for a nice demo. In the actual implementation consider using the barometric coordinate system.
Finding all cliques would be great, but the maximal clique is also ok.
Matrix chain multiplication can be solved efficiently with DP
There's a faster way than the DP approach to solve the LIS problem. It can be solved in O(nlogn) time with a binary search, let's add it to the repo.
Perform a search where you start at nodes A and B and try to meet in the middle. The algorithm should be able to output the edges/nodes to get from point A to point B.
See here for a nice demo. In the actual implementation consider using the barometric coordinate system.
Still need to implement radix sort
See here for a nice demo. In the actual implementation consider using the barometric coordinate system.
Add time complexity to all algorithms in README for ease of lookup.
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