Examples of using Dynamic programming in English and their translations into Slovenian
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Programming
What Is Dynamic Programming?
The problem can be solved by dynamic programming.
The dynamic programming hallmark is the following.
Sequence alignment, Dynamic programming.
Usually, dynamic programming is used for optimization.
Handout 1: Introduction to Dynamic Programming.
Dynamic programming is more efficient than divide and conquer.
Just use Dynamic Programming.
This task can be solved by using dynamic programming.
However, in dynamic programming, the subproblems are interdependent.
This can be solved by dynamic programming.
However, dynamic programming does not solve the subproblems independently.
Memoization is one aspect of Dynamic Programming.
Dynamic programming is applicable when the subproblems are not independent.
Let's use dynamic programming.
The Floyd-Warshall Algorithm is an application of Dynamic Programming.
Dynamic programming is a method for solving problems with overlapping problems.
Memoization is a variation of dynamic programming.
Write the dynamic programming algorithm for the 0-1 Knapsack problem.
That problem is solve using dynamic programming approach.
Dynamic Programming is based on the principle of optimality.
We are going to discuss some common algorithms using dynamic programming.
The dynamic programming relies on a“principle of optimality”.
Efficiency also makes a difference between divide and conquer and dynamic programming.
Dynamic programming, on the other hand, uses the answers of the previous subproblems.
Time consumption is another difference between divide and conquer and dynamic programming.
Dynamic programming is an optimization method based on the principle of optimality.
Python is a general-purpose, high-level, interpreted and dynamic programming language.
Divide and conquer and dynamic programming are two algorithms or approaches to solving problems.
Hence, this is another major difference between divide and conquer and dynamic programming.