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Traveling Salesperson Problem
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1. Introduction

**Traveling Salesperson Problem**: TSP is a problem that tries to find a tour of minimum cost that visits every city once. In this visualization, it is assumed that the underlying graph is a complete graph with (near-)metric distance (meaning the distance function satisfies the triangle inequality) by taking the distance of two points and round it to the nearest integer.

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2. Visualization

View the visualisation of TSP algorithm here.

Originally, all edges in the input graph are colored with the grey.

Throughout the visualization, traversed edge will be highlighted with orange.

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3. Input

There are two different sources for specifying an input graph:

**Draw Graph**: You can put several points on the drawing box, but you must not draw any edge to ensure the (near-)metric property of the graph. After you have finished putting the points, the edges will be drawn automatically for you after.**Example Graphs**: You can select from the list of graphs to get you started.

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4. Bruteforce

**Bruteforce**: It tries all (V-1)! permutation of vertices (not all V! since it does not matter where we start from). It enumerates all possibilities by doing a dfs search with parameters similar to those of __Held-Karp__ algorithm, that is, the DFS search will return the value of the tour starting from current vertex to all vertices that we have not already visited.

Time complexity: O(V * (V - 1)!) = O(V!).

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5. Dynamic Programming

**Dynamic Programming**: It uses a widely known algorithm called Held-Karp. In this visualization, it is implemented as a DFS search that is the same with the bruteforce algorithm, but with memoization to cache the answers. This dramatically brings down the run time complexity to O(2^V * V^2).

Time complexity: O(2^V * V^2).

Note that for N = 10, this algorithm takes roughly ~(100 * 2^10) = 102K operations while the bruteforce algorithm takes roughly ~(10!) = 3628800 operations, around 30 times faster.

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6. Approximation

**Approximation**: There are two approximation algorithms available, a 2-approximation algorithm and a 1.5-approximation algorithm that is also well known as Christofides Algorithm.