Art of the Problem | P = NP Explained Visually (Big O Notation & Complexity Theory) @ArtOfTheProblem | Uploaded 7 years ago | Updated 9 hours ago
A visual explanation of p vs. np and the difference between polynomial vs exponential growth.
Dive deep into the enigma of complexity theory with my exploration of P vs. NP. This video delves into the fundamental principles that govern the computational universe, influenced by the brilliant minds of Von Neumann and Turing.
The origins of the universal machine and the Von Neumann architecture.
The conceptual leap from simple operations to complex algorithms.
How Von Neumann's EDVAC paved the way for modern computing.
The bottlenecks of time and space that challenge computation.
John Nash's groundbreaking perspective on computational growth.
The distinction between Polynomial (P) and Exponential (EXP) time problems.
The intriguing world of "easy to solve" vs. "hard to crack" algorithms.
The captivating realm of NP-complete problems and their significance in computing.
The 'shape of growth curve' and its impact on classifying computational problems.
Nested loops and their contribution to algorithmic complexity.
The concept of one-way functions and their critical role in computer security.
The practical implications of solving NP-complete problems.
The ongoing quest to define the boundary between P and NP.
The million-dollar question that stands at the pinnacle of computer science.
Join us on this intellectual voyage as we unravel the secrets of computational requirements, the intricacy of algorithms, and the pivotal problem that has mystified some of the greatest minds in mathematics and computer science.
Whether you're a seasoned programmer, a mathematics enthusiast, or simply curious about the inner workings of computers, this video is your gateway to understanding one of the most profound questions in computer science: Is P equal to NP?
Support new content: patreon.com/artoftheproblem
A visual explanation of p vs. np and the difference between polynomial vs exponential growth.
Dive deep into the enigma of complexity theory with my exploration of P vs. NP. This video delves into the fundamental principles that govern the computational universe, influenced by the brilliant minds of Von Neumann and Turing.
The origins of the universal machine and the Von Neumann architecture.
The conceptual leap from simple operations to complex algorithms.
How Von Neumann's EDVAC paved the way for modern computing.
The bottlenecks of time and space that challenge computation.
John Nash's groundbreaking perspective on computational growth.
The distinction between Polynomial (P) and Exponential (EXP) time problems.
The intriguing world of "easy to solve" vs. "hard to crack" algorithms.
The captivating realm of NP-complete problems and their significance in computing.
The 'shape of growth curve' and its impact on classifying computational problems.
Nested loops and their contribution to algorithmic complexity.
The concept of one-way functions and their critical role in computer security.
The practical implications of solving NP-complete problems.
The ongoing quest to define the boundary between P and NP.
The million-dollar question that stands at the pinnacle of computer science.
Join us on this intellectual voyage as we unravel the secrets of computational requirements, the intricacy of algorithms, and the pivotal problem that has mystified some of the greatest minds in mathematics and computer science.
Whether you're a seasoned programmer, a mathematics enthusiast, or simply curious about the inner workings of computers, this video is your gateway to understanding one of the most profound questions in computer science: Is P equal to NP?
Support new content: patreon.com/artoftheproblem
