The P versus NP problem is a major unsolved problem in computer science. Informally speaking, it asks whether every problem whose solution can be quickly verified by a computer can also be quickly solved by a computer.
The first mention of the underlying problem occurred in a 1956 letter written by Kurt Gödel to John von Neumann.
P is the class of problems for which there is a deterministic polynomial time algorithm which computes a solution to the problem. NP is the class of problems where there is a nondeterministic algorithm which computes a solution to the problem, but no known deterministic polynomial time solution
The question of whether the complexity class P equals the complexity class NP is one of the most important unsolved problems in computer science. It is not known if P is equal to NP or not. If P equals NP, it would mean that every problem for which a solution can be verified quickly can also be solved quickly. This would have significant implications for cryptography, optimization, and many other fields. However, as of now, it remains an open question.
This is an unsolved problem in computer science; you can't expect to get an answer here, for a problem that nobody on Earth has solved yet! For more information, read the Wikipedia article "P versus NP problem". Briefly, it relates to the question whether or not there are problems that are "hard" to solve, but - once solved - "easy" to verify. This is one of the "millenium problems"; if you can find a proof that P = NP, or (more likely) that it isn't, you can earn a prize of a million dollars.
The question of whether the complexity class P is equal to the complexity class NP is one of the most important unsolved problems in computer science. It is not known if P is equal to NP, and this question is at the heart of the famous P vs. NP problem.
P is the class of problems that can be solved in polynomial time. That is, the size of the input affects the length of the computation multiplicatively. NP is the class of problems in which the effect of input size on the length of the computation is exponential or factorial. In addition, for a problem to be in this class, a proposed or candidate solution must be checkable in polynomial time. The usual example here has to do with multiplication and factoring. You can take two very long prime numbers and quickly multiply them. So multiplication is in P. Given the result of that multiplication, the task of finding its prime factors is not easy. That is, there is no known algorithm that can solve the factoring problem (given very large numbers) in polynomial time. Within the NP class is a subclass consisting of the hardest problems in NP. A problem belonging to this class is called NP-complete. This means that, if a solution can be found to this problem (examples include the travelling salesman problem and the trunk-packing problem), then that solution can be transformed into a solution for all NP problems.
np means no pass, p means pass and i don't know what sp stand for.
No, the keyword "p" is not contained in the set of problems that can be solved in polynomial time, known as NP.
It is still an open question. NP is the class of problems which can be solved in polynomial time by a program run by the theoretical non-deterministic machine. (That is, there is a polynomial upper-bound for the time it would take for the machine to compute the answer, with respect to the size of the input). P is the class of problems which can be solved in polynomial time by a program run by an actual computer (or some abstract model thereof). So far it is not known for sure whether the two classes are the same or not. There are many problems which are known to be NP, and for which no polynomial solution for a real computer is known. However, there is currently no proof that such a solution does not exist (perhaps it does and no one has found it yet). That is why whether P equals NP or not is still an open problem.
Proving that a problem is NP-complete involves demonstrating that it is both in the NP complexity class and that it is at least as hard as any other problem in NP. This typically involves reducing a known NP-complete problem to the problem in question, showing that a solution to the problem in question can be used to solve the known NP-complete problem efficiently.
A problem can be demonstrated to be NP-hard by showing that it is at least as difficult as any other problem in the NP complexity class. This is typically done by reducing a known NP-hard problem to the problem in question, showing that a solution to the problem in question would also solve the known NP-hard problem.
The binomial distribution can be approximated with a normal distribution when np > 5 and np(1-p) > 5 where p is the proportion (probability) of success of an event and n is the total number of independent trials.
House codes:/np @931629/np @709003Bootcamp like codes:/np @172976/np @608368/np @191205/np @842019/np @159932/np @593204/np @145219/np @1450120/np @449496/np @618999/np @801683/np @1014313/np @1444036/np @633644/np @808800/np @1444041Thats all I got sorry if some don't work I didn't check them allIf you want to find me on TFM my user is Butterbe