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Practices of Algorithm for hard problems

Algorithm for hard problems

Practices of Algorithm for hard problems - course 2019-20

1. Description

In this repository the two practices done during the course of algorithm for hard problems have been described carefully. Both practices have been implemented in Java. Apart from providing the perfectly documented code, the sequence of steps to follow will explained in order to compile and execute each practice.

 

2. Practice 1 - Min cut problem

In the first practice the following problem has been resolved:

2.1 Explanation of the problem

For tax reasons Amazon has decided to split its business in two, but wants this to be less detrimental to its sales volume, as some customers may decide not to make a purchase when they have to divide it into two purchases from different suppliers. The partition will be done by selecting (separately) some of the articles for the brand amazon and other for amazonymas. In a first approximation we have the information on which pairs of products have ever been bought together, that is, we have a table of booleans so that T(i,j) is true if the products indexed by i and j indexes have ever been bought together and false in any other case. The aim is to make a partition of the products between Amazon and Amazonymas, so that the number of pairs of products that have been bought together once and are assigned to different suppliers is minimised.

2.2 Implementation

In order to resolve this problem the following steps have been done:

• Design and implement a data structure that stores the data with the necessary attributes for each product.
• Identify the proposed problem as a graph problem, specifically the minimun cut or min cut problem which consists of splitting the vertices of a graph into two disjointed sets with a minimum number of edges between the two pieces.
• Implement an algorithm whose aim is make a partition close to the optimum of the products. For this purpose, the Karger probabilistic algorithm has been programmed.
• Test the implemented algorithm with a reasonable number of data and with different random generators.
• Implement the Karger-Stein algorithm, which is an improvement on Karger's algorithm.
• Extend the algorithm to allow cases where products can be purchased more than once together, minimally justifying the performance achieved with the new algorithm.

2.3 Compilation and execution of the source code

The code of the practice is located in Practica 1 and is composed by the following files:

• gen_tests_cases.py is a python script used to generate the randomized test cases to verify the behaviour of the code.
• TestSets is a folder which contains the tests cases.
• src is a folder are located the java files.
• ejecutar1.sh is a bash script which allows the user to compile and execute the min-cut problem using karger or karger-Stein algorithms with different tests cases and random number generators.
• MemoriaP1_APD.pdf is a report which explains with so much detail all the work done.
• LEEME.md which contains the instructions (commands) to compile and execuete code.

 

3. Practice 2 - A personal version of bzip2 compressor

In the second practice the following problem has been resolved:

3.1 Explanation of the problem

The Burrows-Wheeler transform (BWT) converts any string into a string with a much higher frequency of adjacent equal symbols, and this process is reversible. one of its common uses is in text compression, as it is used in conjunction with Move-To-Front and Huffman to obtain the popular bzip2 compressor. The most common way to calculate the BWT is by properly ordering the input chain rotations. This can be done directly at a cost of O(n2logn), where the length of the chain, or much efficiently with cost in quasi-linear time using suffix vectors.

3.1 Implementation of the problem

In order to resolve this problem the following steps have been done:

• Implement the suffix vector data structure as efficiently as possible.
• Implement the Burrows-Wheeler transformation using suffix vectors, trying to reach a quasi-linear cost in time.
• Implement the compression algorithm Move-to-Front.
• Test all the implemented algorithms with a sufficient number of interesting data and of sufficient size.
• Using some Huffman public implementation, combine BWT, Move-To-Front and Huffman to obtain a similar abzip2 compressor which has been named like bzip3.

3.3 Compilation and execution of the source code

The code of the practice is located in Practica 2 and is composed by the following files:

• compile.sh is a bash script which allows to compile the java code files and also the Huffman code written in C++.
• bzip3.ah is a bash script which allows to execute the compressor.
• test_bzip3.sh is a bash script which test the bzip3 compressor realising compressions and uncompressions of files and verifying if both are equal. Apart from that the time spent in each process is shown to the user too.
• ejecutar2.sh is a bash script which allows the user to compile and execute the compressor automatically.
• LEEME.md is a file that contains the instructions (commands) to compile and execuete code.
• Tets is a folder which stores the different files used to test the compressor.
• src is the folder where the code java files are located.
• Huffman is the directory where is stored the C++ implementation Huffman compressor.
• MemoriaP2_APD.pdf is a report which explains with so much detail all the work done.

 

4. Authors

• Victor Peñasco - [email protected]
• Rubén Rodríguez - [email protected]