Once #30 is complete see if the performance can be improved by using a dynamic tree to obtain O(EVlogV) time complexity.

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

See here for a nice demo. In the actual implementation consider using the barometric coordinate system.

Add algorithm to find Eigen vectors/values along with a complete set of unit tests.

The traveling salesman problem can be solved in O(n!) with a brute force but can be reduced to (n²2ⁿ) with DP.

Matrix chain multiplication can be solved efficiently with DP

Edit .travis.yml file to setup CI on travis-ci.org for more than just broken links

Finding all cliques would be great, but the maximal clique is also ok.

Still need to implement radix sort

See here for a nice demo. In the actual implementation consider using the barometric coordinate system.

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

See here for a nice demo. In the actual implementation consider using the barometric coordinate system.

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?

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.

Add quickselect algorithm. Quickselect is a selection algorithm to find the kth smallest element in an unordered list.

Still need to code up and document usage of how to use the following algorithms. Help is of course always appreciated :)

Graph Theory

Tree related

• Centroid decomposition
• Heavy-Light decomposition
  • Distance between two nodes
  • Lowest common ancestor
• Tree Center(s)
• Tree Diameter
• Tree Isomorphism/canonical form, O(V+E)
• Rooting tree

Strings

• Aho-Corasick
• Boyer-Moore
• Burrows Wheeler
• Longest repeated substring(s)
• Longest common substring(s)
• Suffix array LCP Kasai algorithm
• Suffix array string match O(mlogn)
• Suffix array string match O(m + logn)
• Z-Algorithm

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.

Add line segment intersection and test against:
https://open.kattis.com/problems/segmentintersection

Add time complexity to all algorithms in README for ease of lookup.