Chapter 8: Greedy Algorithm

In technical terms: at each step you pick the locally optimal solution, and in the end you’re left with the globally optimal solution.
greedy algorithms don’t always work.
A set is like a list, except that each item can show up only once in a set. Sets can’t have duplicates.
NP-complete problems
- NP-complete problems have no known fast solution.
- If you have an NP-complete problem, your best bet is to use an approximation algorithm.
- Greedy algorithms are easy to write and fast to run, so they make good approximation algorithms.
```
arr = [1, 2, 2, 3, 3, 3]

result = set(arr)

print(result) =>set([1, 2, 3])
```

Last updated 5 years ago

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In technical terms: at each step you pick the locally optimal solution, and in the end you’re left with the globally optimal solution.
greedy algorithms don’t always work.
A set is like a list, except that each item can show up only once in a set. Sets can’t have duplicates.
NP-complete problems
- NP-complete problems have no known fast solution.
- If you have an NP-complete problem, your best bet is to use an approximation algorithm.
- Greedy algorithms are easy to write and fast to run, so they make good approximation algorithms.
```
arr = [1, 2, 2, 3, 3, 3]

result = set(arr)

print(result) =>set([1, 2, 3])
```

Last updated 5 years ago

Was this helpful?