mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-18 09:10:16 +00:00
fe5c711ce6
* Rewrite parts of Vector and Matrix methods * Refactor determinant method and add unit tests Refactor determinant method to create separate minor and cofactor methods. Add respective unit tests for new methods. Rename methods using snake case to follow Python naming conventions. * Reorganize Vector and Matrix methods * Update linear_algebra/README.md Co-authored-by: John Law <johnlaw.po@gmail.com> * Fix punctuation and wording * Apply suggestions from code review Co-authored-by: John Law <johnlaw.po@gmail.com> Co-authored-by: John Law <johnlaw.po@gmail.com>
33 lines
1.5 KiB
Markdown
33 lines
1.5 KiB
Markdown
# A naive recursive implementation of 0-1 Knapsack Problem
|
||
|
||
This overview is taken from:
|
||
|
||
https://en.wikipedia.org/wiki/Knapsack_problem
|
||
|
||
---
|
||
|
||
## Overview
|
||
|
||
The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.
|
||
|
||
The knapsack problem has been studied for more than a century, with early works dating as far back as 1897 The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.
|
||
|
||
---
|
||
|
||
## Documentation
|
||
|
||
This module uses docstrings to enable the use of Python's in-built `help(...)` function.
|
||
For instance, try `help(Vector)`, `help(unit_basis_vector)`, and `help(CLASSNAME.METHODNAME)`.
|
||
|
||
---
|
||
|
||
## Usage
|
||
|
||
Import the module `knapsack.py` from the **.** directory into your project.
|
||
|
||
---
|
||
|
||
## Tests
|
||
|
||
`.` contains Python unit tests which can be run with `python3 -m unittest -v`.
|