filmov
tv
What are pseudo-polynomial run times? | Knapsack Dynamic Programming
Показать описание
Learn why the knapsack and subset sum dynamic programming algorithms are actually exponential rather than polynomial time algorithms, and why we refer to this type of algorithm as having a pseudo-polynomial runtime.
The relation of pseudo-polynomial algorithms to NP-Completeness reductions through NP-Hard combinatorial optimization problems and P != NP is also covered.
The relation of pseudo-polynomial algorithms to NP-Completeness reductions through NP-Hard combinatorial optimization problems and P != NP is also covered.
0:06:10
What are pseudo-polynomial run times? | Knapsack Dynamic Programming
0:07:13
Polynomial vs. Pseudo-Polynomial
0:03:06
Pseudo-polynomial time
0:06:59
Why is the knapsack problem pseudo-polynomial?
0:02:11
Computer Science: Do I understand pseudo polynomial time correctly? (2 Solutions!!)
0:13:06
CS 5720 L16 07 Knapsack Complexity and Pseudopolynomial
0:01:17
Expressing pseudo-polynomial runtime solely in terms of the input size
0:02:20
What is the precise definition of pseudo-polynomial time (feat. Counting Sort)
0:02:26
Computer Science: About the pseudo polynomial complexity of the KnapSack 0/1 problem
0:02:04
Computer Science: Shouldn't every algorithm run in pseudo-polynomial time? (2 Solutions!!)
0:03:18
Pseudopolynomial Algorithm For Knapsack Solution - Intro to Theoretical Computer Science
0:02:38
Pseudopolynomial Algorithm For Knapsack - Intro to Theoretical Computer Science
0:01:56
Pseudo-polynomial time algorithm for NP-Complete Problems
0:01:21
Ford-Fulkerson pseudo-polynomial
0:05:13
Big-O notation in 5 minutes
1:01:28
Karl Bringmann: Pseudopolynomial-time Algorithms for Optimization Problems
0:49:11
F2021 CS 340 Lecture 34 (0-1 Knapsack DP Algorithm, Backtracing, Pseudopolynomial, Brute Force)
0:00:50
P = NP? - Intro to Theoretical Computer Science
0:02:40
Polynomial Time Approximation Schemes - Intro to Theoretical Computer Science
0:01:59
Polynomial Or Exponential Running Time Solution - Intro to Theoretical Computer Science
1:22:34
Beyond Worst-Case Analysis (Lecture 15: Smoothed Complexity and Pseudopolynomial-Time Algorithms)
1:03:45
18. Dynamic Programming, Part 4: Rods, Subset Sum, Pseudopolynomial
0:00:26
P = NP? - Intro to Theoretical Computer Science
0:30:43
Computational Complexity of the Knapsack Problem: P vs. NP, NP Completeness and Lemmings
Комментарии