Summary of The Secret Link Between Thousands of Unsolved Math Problems

00:00:00 - 00:30:00

The video introduces the concept of NP completeness, explaining that solving just one of the thousands of unsolved math problems linked to it could unlock unimaginable advancements in technology, security, and optimization. Some of these problems, referred to as NP-complete, have practical applications such as organizing parties, finding optimal hospital locations, and predicting protein folding. The video demonstrates the limitations of the brute force algorithm to solve the Clique problem, which is a famous NP-complete problem. It then delves into the groundbreaking work of Stephen Cook and Leonard Levin, who revealed the deep connection between a famous NP problem called SAT and computation, showing that the SAT problem could carry out computation. The video discusses the technique of reduction, which is used by computer scientists when faced with a new hard problem, and how it can be quite technical. The question of whether P equals NP remains the biggest open question in computer science, as it would imply a huge shift in world view if it were proven.

• 00:00:00 In this section, the video introduces the concept of NP completeness and how solving just one of the thousands of unsolved math problems linked to it could unlock unimaginable advancements in technology, security, and optimization. The problems, referred to as NP-complete, are of such complexity that no general algorithm currently exists to solve them in a reasonable amount of time for large-scale instances, despite having practical applications such as organizing parties, finding optimal hospital locations, and predicting protein folding. The video demonstrates the limitations of the brute force algorithm to solve the Clique problem, which is a famous NP-complete problem that seeks to find a set of nodes in a graph that form a completely connected subgraph of a given size.
• 00:05:00 In this section, the video explains that the Clique problem is hard because it runs exponentially as the input grows larger. Computer scientists have not found an algorithm that scales faster than exponential, making it an intractable problem. A solution to the Clique problem would have to be a clever and faster algorithm that scales polynomially, where the input number is in the base and a constant is in the exponent, making the algorithm grow much slower as the input grows. These problems fall into the class of NP or non-deterministic polynomial, meaning they can be solved by a non-deterministic polynomial time algorithm but verified in polynomial time. These problems are sorted into classes based on types of algorithms and resources they use.
• 00:10:00 In this section, the transcript discusses the role of non-deterministic computation as a theoretical tool to better understand problems. While not a practical model of computation, non-deterministic computation provides an expressive model that allows researchers to characterize all the problems that have guessing as the hard part. This idea mathematically captures being hard to solve but easy to check. The section then delves into the groundbreaking work of Stephen Cook and Leonard Levin, who revealed the deep connection between a famous NP problem called sat and computation, showing that the sat problem could carry out computation, similar to how Descartes united algebra and geometry by showing that they were different representations of the same thing.
• 00:15:00 In this section, the speaker explains how Boolean algebra works and its use in solving Boolean satisfiability problems (SAT). A SAT is a type of problem where one needs to find the correct combination of true and false values for variables in a Boolean formula so that the entire formula returns a one. The speaker demonstrates how to use Boolean algebra to solve a SAT problem and finds the right combination of activities for a party given certain constraints. SAT is a type of problem that is difficult to solve efficiently, and it is an NP problem, meaning we don't have a guaranteed significantly better way to solve it than by checking every single combination of true and false. However, once a solution is presented, it is easy to verify if it is correct. SAT formulas are incredibly expressive and can be used to describe all kinds of constraints and objects, making them a powerful tool in mathematics.
• 00:20:00 In this section, the video explains how a Turing machine can mathematically capture the concept of computation, and goes on to discuss Cook and Levin's theorem, which shows that a SAT formula can represent any non-deterministic Turing machine. The video provides a simple toy example of a Turing machine that contains only one cell on the tape and can return "yes" or "no" depending on whether a 1 or 0 is written in the cell, respectively. The behavior of this Turing machine is captured by a SAT formula that includes variables representing the state of the machine and the symbol on the tape at each time step. The video explains how the variables in the formula need to be true for the formula to be true, and notes that some aspects of the Turing machine's behavior are not captured by the formula.
• 00:25:00 In this section of the video, the narrator explains how Cook and Levin developed formulas to represent the constraints of a Turing machine in a SAT formula. This SAT formula can represent any Turing machine, even a non-deterministic one. Solving the SAT problem would solve all other NP problems, making SAT the first ever NP-complete problem. Richard Karp later showed that another 21 problems, including the Clique problem, were also NP-complete in the same way as SAT. Thousands of NP-complete problems have since been identified in various fields, and NP completeness unifies all of these problems, showing that the fundamental difficulty in solving them is the same. The question of whether P equals NP remains the biggest open question in computer science, as it would imply a huge shift in world view if it were proven.
• 00:30:00 In this section, the video discusses the technique of reduction, which is used by computer scientists when faced with a new hard problem. They try to reduce a known NP-complete problem to the new problem in order to determine whether or not to spend time solving it. Reduction is one of the most important techniques in theoretical computer science, but it can be quite technical. The video creator has made a bonus video on their streaming platform, Nebula, where they show how to reduce the Stat problem to The Clique problem. Nebula subscribers can also access a ton of exclusive high production content by some of their favorite creators, including Real Engineering and Real Science. Signing up for Nebula is the best way to support the channel and the creation of educational content.