We have actually been writing about efficient algorithms to solve complex problems, prefer shortest path, Euler graph, minimum extending tree, etc. Those were all success story of algorithm designers. In this post, failure stories of computer system science are discussed.

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Can all computational difficulties be fixed by a computer? There room computational problems that can not be addressed by algorithms even with endless time. For example Turing Halting difficulty (Given a program and also an input, even if it is the program will at some point halt as soon as run v that input, or will run forever). Alan Turing confirmed that a general algorithm to settle the halting difficulty for all possible program-input pairs cannot exist. A an essential part of the proof is, Turing maker was used as a mathematical definition of a computer and program (Source Halting Problem).Status of NP finish problems is another failure story, NP finish problems are troubles whose condition is unknown. No polynomial time algorithm has actually yet been uncovered for any type of NP finish problem, nor has actually anybody yet been able to prove that no polynomial-time algorithm exist for any kind of of them. The interesting component is, if any kind of one of the NP finish problems deserve to be solved in polynomial time, then every one of them can be solved.
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What room NP, P, NP-complete and NP-Hard problems?P is a set of difficulties that can be solved by a deterministic Turing device in Polynomial time.

NP is collection of decision difficulties that can be solved by a Non-deterministic Turing device in Polynomial time. Ns is subset that NP (any problem that deserve to be addressed by a deterministic machine in polynomial time can also be addressed by a non-deterministic an equipment in polynomial time).Informally, NP is a set of decision difficulties that can be fixed by a polynomial-time via a “Lucky Algorithm”, a wonder algorithm that always makes a best guess amongst the given set of options (Source Ref 1).NP-complete difficulties are the hardest difficulties in the NP set. A decision problem L is NP-complete if:1) together is in NP (Any offered solution for NP-complete difficulties can be showed quickly, however there is no reliable known solution).2) Every trouble in NP is reducible to together in polynomial time (Reduction is characterized below).A problem is NP-Hard if it complies with property 2 mentioned above, doesn’t should follow residential or commercial property 1. Therefore, the NP-Complete collection is likewise a subset of the NP-Hard set. Decision vs Optimization ProblemsNP-completeness uses to the kingdom of decision problems. It was set up this way because it’s much easier to to compare the an obstacle of decision troubles than the of optimization problems. In reality, though, being able to settle a decision difficulty in polynomial time will regularly permit us to fix the equivalent optimization problem in polynomial time (using a polynomial variety of calls to the decision problem). So, pointing out the difficulty of decision problems is regularly really tantamount to discussing the an obstacle of optimization problems.(Source Ref 2).For example, consider the peak cover problem (Given a graph, uncover out the minimum size vertex collection that covers all edges). It is an optimization problem. Matching decision trouble is, given undirected graph G and also k, is there a peak cover of dimension k?What is Reduction?Let L1 and L2 be 2 decision problems. Mean algorithm A2 solves L2. That is, if y is one input because that L2 climate algorithm A2 will certainly answer yes or No depending on whether y belongs come L2 or not.The idea is to discover a transformation from L1 to L2 so the algorithm A2 can be part of an algorithm A1 to settle L1. Learning reduction, in general, is really important. For example, if we have library attributes to solve specific problems and if we have the right to reduce a brand-new problem to one of the fixed problems, we save a most time. Consider the instance of a difficulty where we have actually to find the minimum product path in a offered directed graph wherein the product of route is the multiplication of weights the edges follow me the path. If we have actually code because that Dijkstra’s algorithm to find the shortest path, we can take the log in of every weights and use Dijkstra’s algorithm to find the minimum product path quite than writing a fresh password for this brand-new problem.How to prove the a given trouble is NP complete?From the an interpretation of NP-complete, it shows up impossible toprove the a difficulty L is NP-Complete. Through definition, it calls for us to that show every trouble in NP in polynomial time reducible come L. Fortunately, over there is an alternate means to prove it. The idea is to take it a recognized NP-Complete problem and also reduce it come L. If polynomial time reduction is possible, we can prove the L is NP-Complete by transitivity of palliation (If a NP-Complete difficulty is reducible to l in polynomial time, climate all troubles are reducible to l in polynomial time).

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What was the an initial problem verified as NP-Complete?There must be some an initial NP-Complete difficulty proved by definition of NP-Complete problems. Satellite (Boolean satisfiability problem) is the an initial NP-Complete difficulty proved by cook (See CLRS book for proof).It is constantly useful to know about NP-Completeness also for engineers. Intend you space asked to write an efficient algorithm to solve really important difficulty for your company. After ~ a lot of thinking, you can only come up exponential time method which is impractical. If you don’t know about NP-Completeness, you can only say that I might not come through an reliable algorithm. If you know about NP-Completeness and also prove the 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, climate that equipment solves a huge problem of computer science many scientists have been trying for years.We will shortly be discussing more NP-Complete problems and also their proof because that NP-Completeness.References:MIT video Lecture ~ above Computational ComplexityIntroduction come Algorithms 3rd Edition by Clifford Stein, thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivesthttp://www.ics.uci.edu/~eppstein/161/960312.htmlPlease write comments if you find anything incorrect, or you want to share an ext information about the topic disputed above