Want this question answered?
Be notified when an answer is posted
You can use the bounded tiling problem. Given a problem in NP A, it has a turing machine M that recognizes its language. We construct tiles for the bounded tiling problem that will simulate a run of M. Given input x, we ask if there is a tiling of the plane and answer that will simulate a run of M on x. We answer true iff there is such a tiling.
What have we got to prove? Whether we have to prove a triangle as an Isoseles triangle or prove a property of an isoseles triangle. Hey, do u go to ALHS, i had that same problem on my test today. Greenehornet15@yahoo.com
The problem is complete. There is enough information there for it to be solved.
The answer will depend on what the problem is: some can be solved using an array but for others, arrays are a complete waste of time.
At first we have to identify the cause(root) of the problem and then find the accurate solution for it, so that it cant repeat again.we have to use our complete resources for finding the solution of the problem
Yes, it is possible to prove that the clique problem is NP-complete.
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.
The Knapsack Problem is NP-complete. This means that it is a problem in computational complexity theory that belongs to the NP complexity class and is at least as hard as the hardest problems in NP. It is a classic optimization problem where the goal is to maximize the total value of items placed into a knapsack without exceeding the knapsack's capacity. The NP-completeness of the Knapsack Problem has been proven through reductions from other NP-complete problems such as the Boolean Satisfiability Problem.
Yes, the clique problem is NP-complete.
Yes, the partition problem is NP-complete.
Yes, prime factorization is not an NP-complete problem. It is in fact in the complexity class NP, but it is not known to be NP-complete.
The proof that the Clique Problem is NP-complete involves showing that it is both in the NP complexity class and that it is as hard as any problem in NP. This is typically done by reducing a known NP-complete problem, such as the SAT problem, to the Clique Problem in polynomial time. This reduction demonstrates that if a polynomial-time algorithm exists for the Clique Problem, then one also exists for the known NP-complete problem, which implies that the Clique Problem is NP-complete.
Yes, interval scheduling is an NP-complete problem.
Yes, the path selection problem is NP-complete.
One can demonstrate that a problem is NP-complete by showing that it belongs to the NP complexity class and that it is at least as hard as any other problem in NP. This can be done by reducing a known NP-complete problem to the problem in question through a polynomial-time reduction.
Yes, the problem of subgraph isomorphism is NP-complete.
Yes, the problem of determining whether a given path exists in a graph can be demonstrated as NP-complete by reducing it to a known NP-complete problem, such as the Hamiltonian path problem. This reduction shows that the path existence problem is at least as hard as the known NP-complete problem, making it NP-complete as well.