Chapter 9: Dynamic Programming

Every dynamic-programming solution involves a grid.
The values in the cells are usually what you’re trying to optimize. For the knapsack problem, the values were the value of the goods.
Each cell is a subproblem, so think about how you can divide your problem into subproblems. That will help you figure out what the axes are.
Dynamic programming is useful when you’re trying to optimize something given a constraint.
You can use dynamic programming when the problem can be broken into discrete subproblems.
Every dynamic-programming solution involves a grid.
The values in the cells are usually what you’re trying to optimize.
Each cell is a subproblem, so think about how you can divide your problem into subproblems.
There’s no single formula for calculating a dynamic-programming solution.

Implementation：

git diff
Biologists use the longest common subsequence to find similarities in DNA strands

Last updated 5 years ago

Was this helpful?

Every dynamic-programming solution involves a grid.
The values in the cells are usually what you’re trying to optimize. For the knapsack problem, the values were the value of the goods.
Each cell is a subproblem, so think about how you can divide your problem into subproblems. That will help you figure out what the axes are.
Dynamic programming is useful when you’re trying to optimize something given a constraint.
You can use dynamic programming when the problem can be broken into discrete subproblems.
Every dynamic-programming solution involves a grid.
The values in the cells are usually what you’re trying to optimize.
Each cell is a subproblem, so think about how you can divide your problem into subproblems.
There’s no single formula for calculating a dynamic-programming solution.

Implementation：

git diff
Biologists use the longest common subsequence to find similarities in DNA strands

Last updated 5 years ago

Was this helpful?