** NP-complete** problems are a subset of the larger class of

**.**

**NP (nondeterministic polynomial time) problems****problems are a class of computational problems that can be solved in polynomial time by aÂ non-deterministic machine and can be verified in polynomial time by a deterministic Machine. A problem**

**NP****in**

**L****is**

**NP****if all other problems in**

**NP-complete****can be reduced to**

**NP****in polynomial time. If any**

**L****problem can be solved in polynomial time, then every problem in**

**NP-complete****can be solved in polynomial time.**

**NP****Â problems are the hardest problems in theÂ**

**NP-complete****Â set.**

**NP**A decision problem ** L** is

**if it follow the below two properties:**

**NP-complete**is in**L**(Any solution to NP-complete problems can be checked quickly, but no efficient solution is known).**NP**- Every problem in
is reducible to**NP**in polynomial time (Reduction is defined below).Â**L**

A problem is ** NP-Hard** if it obeys Property 2 above and need not obey Property 1. Therefore, a problem is

**if it is both**

**NP-complete****and**

**NP****.**

**NP-hard****Decision vs Optimization Problems**Â

**Decision vs Optimization Problems**

NP-completeness applies to the realm of decision problems. Â It was set up this way because itâ€™s easier to compare the difficulty of decision problems than that of optimization problems. Â Â In reality, though, being able to solve a decision problem in polynomial time will often permit us to solve the corresponding optimization problem in polynomial time (using a polynomial number of calls to the decision problem). So, discussing the difficulty of decision problems is often really equivalent to discussing the difficulty of optimization problems.Â

For example, consider the vertex cover problem (Given a graph, find out the minimum sized vertex set that covers all edges). It is an optimization problem. The corresponding decision problem is, given undirected graph G and k, is there a vertex cover of size k?Â

**What is Reduction?**Â

**What is Reduction?**

Let ** L1** and

**be two decision problems. Suppose algorithm**

**L2****solves**

**A2****. That is, if**

**L2****is an input for**

**y****then algorithm**

**L2****will answer Yes or No depending upon whether**

**A2****belongs to**

**y****or not.**

**L2**The idea is to find a transformation from

**to**

**L1****so that algorithm**

**L2****can be part of algorithm**

**A2****to solve**

**A1****.**

**L1**Â

Learning reduction, in general, is very important. For example, if we have library functions to solve certain problems and if we can reduce a new problem to one of the solved problems, we save a lot of time. Consider the example of a problem where we have to find the minimum product path in a given directed graph where the product of the path is the multiplication of weights of edges along the path. If we have code for Dijkstraâ€™s algorithm to find the shortest path, we can take the log of all weights and use Dijkstraâ€™s algorithm to find the minimum product path rather than writing a fresh code for this new problem.

**How to prove that a given problem is NP-complete?**Â

**How to prove that a given problem is NP-complete?**

From the definition of NP-complete, it appears impossible toÂ prove that a problem ** L **is NP-Complete. Â By definition, it requires us to that show every problem in NP is polynomial time reducible to

**. Â Fortunately, there is an alternate way to prove it. Â The idea is to take a known NP-Complete problem and reduce it to**

**L****. If a polynomial-time reduction is possible, we can prove that**

**L****is NP-Complete by transitivity of reduction (If an NP-Complete problem is reducible to L in polynomial time, then all problems are reducible to L in polynomial time).Â**

**L****What was the first problem proved as NP-Complete?**Â

**What was the first problem proved as NP-Complete?**

There must be some first NP-Complete problem proved by the definition of NP-Complete problems. Â SAT (Boolean satisfiability problem) is the first NP-Complete problem proved by Cook (See CLRS book for proof).Â

It is always useful to know about NP-Completeness even for engineers. Suppose you are asked to write an efficient algorithm to solve an extremely important problem for your company. After a lot of thinking, you can only come up exponential time approach which is impractical. If you don’t know about NP-Completeness, you can only say that I could not come up with an efficient algorithm. If you know about NP-Completeness and prove that the problem is NP-complete, you can proudly say that the polynomial-time solution is unlikely to exist. If there is a polynomial-time solution possible, then that solution solves a big problem of computer science many scientists have been trying for years.Â

**NP-Complete problems and their proof for NP-Completeness.Â **

**NP-Complete problems and their proof for NP-Completeness.Â**

- Prove that SAT is NP Complete
- Prove that Sparse Graph is NP-Complete
- Prove that KITE is NP-Complete
- Prove that Hamiltonian Cycle is NP-Complete
- Subset Sum is NP Complete
- Prove that Collinearity Problem is NP Complete
- Set partition is NP complete
- Hitting Set problem is NP Complete
- 3-coloring is NP Complete
- Set cover is NP Complete
- Optimized Longest Path is NP Complete
- Double SAT is NP Complete
- Prove that 4 SAT is NP complete
- Prove that Dense Subgraph is NP Complete by Generalisation
- Prove that a problem consisting of Clique and Independent Set is NP Complete
- Prove that Almost-SAT is NP Complete
- Prove that MAX-SAT is NP Complete
- Prove Max2SAT is NP-Complete by Generalisation
- Subset Equality is NP Complete
- Hitting Set problem is NP Complete