Updated README file with installation and usage instructions

README.md

-# infomads-project
+    
+# Cinema Seating problem solver _Team name TODO_ 
 
-## Manual
 
-Run main.py with the filename of the input relative to the parent folder of main.py as the first argument.
+This program aims to efficiently solve the Cinema Seating problem as define [in this document](./cinema-assignment.pdf).
+
+## Installation
+
+An installation of Python 3.8 is required, which can be downloaded from [here](https://www.python.org/downloads/).
+
+The two Python libraries Numpy and MIP are used throughout the progam.
+These dependencies can be installed using the Python package manager [pip](https://pip.pypa.io/en/stable/) (Bundled with Python 3.8).
+
+#####Numpy installation command:
+
+```bash
+pip install numpy
+```
+
+#####MIP installation command:
+
+```bash
+pip install mip
+```
+
+Furthermore, the MIP library can run as an independent ILP solver, however, it can also leverage the faster commercial
+ILP solver Gurobi, for which an installation guide can be found [here](https://www.gurobi.com/)
+
+## Usage
+
+The cli interface of the program is defined as follows:
+
+```bash
+python3 main.py [arguments] file-path
+
+arguments:
+ [-online | -online-comp] -> overwrite the default of running offline problems and algorithms
+    * online:      use online input format and online algorithm
+    * online-comp: use offline input, find optimal solution, and then run online algorithm to estimate comp-ration
+ [-a <name>]              -> algorithm to be used
+ [-benchmark <i> <p> <o>] -> benchmarks until problem p, every problem i iterations, results saved in results file o
+``` 
+
+By default the program tries to solve the problem in the file given with argument 'file-path' with an offline problem solver.
+This can also be set using the '-online' argument to use online algorithms instead.
+
+The available algorithms that can be used are the following:
+
+Offline:
+   * branch-bound
+   * ilp (default)
+   * naive (broken)
+
+Online:
+   * greedy
+   * least-lost-seats (default)
+
+So running a greedy algorithm can be done with the following command:
+
+```bash
+python3 main.py -online -a greedy TestCases/Online1.txt
+```
+
+Running a benchmark over 7 iterations for every test case until Exact10.txt using the ILP can be done with the 
+following command:
+
+```bash
+python3 main.py [-a ilp] -benchmark 7 10 ilp-benchmark-7-iterations-until-10
+```
+
+The results of the benchmark will be saved to a newly created file in the ``/input`` folder. The name of the csv file
+is the name specified in the command with the epoch prepended to the file name. 
+
+## License
+[MIT](https://choosealicense.com/licenses/mit/)
 
-For example: ```$ python3 main.py input.txt```
 
 ## References
 - Graph Theory