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.

Matrix chain multiplication can be solved efficiently with DP

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

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

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?

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

Still need to implement radix sort

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

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

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

Use Kőnig's theorem.

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

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.

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.

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

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