Podcast
Questions and Answers
Pandas is a Python package that provides fast, flexible, and expressive ______ structures designed to make working with data both easy and intuitive.
Pandas is a Python package that provides fast, flexible, and expressive ______ structures designed to make working with data both easy and intuitive.
data
The general knapsack problem is an example of ______ programming.
The general knapsack problem is an example of ______ programming.
integer
To read an Excel file in Python, we can use the ______ function from the pandas library.
To read an Excel file in Python, we can use the ______ function from the pandas library.
read_excel
We can convert a DataFrame into a tuple list using the ______ function in pandas.
We can convert a DataFrame into a tuple list using the ______ function in pandas.
The Greedy Algorithm is ______ to implement, but does not always yield the best solution.
The Greedy Algorithm is ______ to implement, but does not always yield the best solution.
In the general knapsack problem, we are allowed to take multiple items of each type, and all items of a given type have the same ______.
In the general knapsack problem, we are allowed to take multiple items of each type, and all items of a given type have the same ______.
The knapsack function takes two arguments: item_list and ______.
The knapsack function takes two arguments: item_list and ______.
The item list is a list of tuples containing the item name, ______, and profit.
The item list is a list of tuples containing the item name, ______, and profit.
The knapsack_by_profit function sorts the item list by descending order considering the ______.
The knapsack_by_profit function sorts the item list by descending order considering the ______.
The knapsack_by_weight function sorts the item list by ascending order considering the ______.
The knapsack_by_weight function sorts the item list by ascending order considering the ______.
The knapsack function is called with the sorted item list and ______ as arguments.
The knapsack function is called with the sorted item list and ______ as arguments.
The greedy algorithm is used to solve the ______ problem.
The greedy algorithm is used to solve the ______ problem.
The knapsack_by_weight function sorts the item list by ______ order considering the profit/weight.
The knapsack_by_weight function sorts the item list by ______ order considering the profit/weight.
The greedy algorithm considers the ______ profit-weight ratio to find the optimal solution.
The greedy algorithm considers the ______ profit-weight ratio to find the optimal solution.
The function knapsack_by_profit_per_weight is defined to take ______ and capacity as arguments.
The function knapsack_by_profit_per_weight is defined to take ______ and capacity as arguments.
The sorted_items list is sorted by the ______ of the item's profit and weight.
The sorted_items list is sorted by the ______ of the item's profit and weight.
The optimal solution is not always found by making ______ locally optimal choices.
The optimal solution is not always found by making ______ locally optimal choices.
Increasing the capacity to ______ may change the optimal solution found by the greedy algorithm.
Increasing the capacity to ______ may change the optimal solution found by the greedy algorithm.
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Study Notes
Importing Data from Excel to Python
- Importing pandas library:
import pandas as pd - Reading Excel file:
df = pd.read_excel("kpData.xlsx") - Converting DataFrame to tuple list:
item_list = list(df.itertuples(index=False, name=None))
Pros and Cons of Greedy Algorithm
- Easy to implement
- Computationally efficient
- Does not always yield the best solution
- Unknown quality of the approximation
General Knapsack Problem
- Allowed to take multiple items of each type
- Not a 0/1 programming problem
- Integer programming problem
- Example: Cargo Loading Problem
Homework 1: Knapsack Problem
- Define a function
knapsackwith argumentsitem_listandcapacity - Check the weights of the items in the item list
- If the weight is less than capacity, add the item to the knapsack and decrease the capacity by the weight
- Increase the profit
- Loop ends when all items are checked
- Call the function
Greedy Algorithm Examples
- Example 1: C = 20, item list =
[("clock", 175, 10), ...] - Greedy algorithm 2: Sort item list by descending order of profit
- Greedy algorithm 3: Sort item list by ascending order of weight
- Greedy algorithm 4: Sort item list by descending order of profit/weight ratio
Comparing Greedy Algorithms
- Applied different rules: by given order, by highest profit, by highest profit-weight ratio, by lowest weight
- Results: profit = 275, 200, 255, 175 respectively
- None of them give the optimal answer
Limitations of Greedy Algorithm
- Sequence of locally "optimal" choices does not always yield a globally optimal solution
- Greedy algorithm by given order is not always the winner
- Profit-weight ratio is often the winner with larger data sets
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