scholarly journals Testing a new idea to solve the P = NP problem with mathematical induction

Author(s):  
Yubin Huang

Background. P and NP are two classes (sets) of languages in Computer Science. An open problem is whether P = NP. This paper tests a new idea to compare the two language sets and attempts to prove that these two language sets consist of same languages by elementary mathematical methods and basic knowledge of Turing machine. Methods. By introducing a filter function C(M,w) that is the number of configurations which have more than one children (nondeterministic moves) in the shortest accept computation path of a nondeterministic Turing machine M for input w, for any language L(M) ∈ NP, we can define a series of its subsets, Li(M) = {w | w ∈ L(M) ∧ C(M,w) ≤ i}, and a series of the subsets of NP as Li = {Li(M) | ∀M ∙ L(M) ∈ NP}. The nondeterministic multi-tape Turing machine is used to bridge two language sets Li and Li+1, by simulating the (i+1)-th nondeterministic move deterministically in multiple work tapes, to reduce one (the last) nondeterministic move. Results. The main result is that, with the above methods, the language set Li+1, which seems more powerful, can be proved to be a subset of Li. This result collapses Li ⊆ P for all i ∈ N. With NP = ⋃i∈NLi, it is clear that NP ⊆ P. Because by definition P ⊆ NP, we have P = NP. Discussion. There can be other ways to define the subsets Li and prove the same result. The result can be extended to cover any sets of time functions C, if ∀f ∙ f ∈ C ⇒ f2 ∈ C, then DTIME(C) = NTIME(C). This paper does not show any ways to find a solution in P for the problem known in NP.

2015 ◽  
Author(s):  
Yubin Huang

Background. P and NP are two classes (sets) of languages in Computer Science. An open problem is whether P = NP. This paper tests a new idea to compare the two language sets and attempts to prove that these two language sets consist of same languages by elementary mathematical methods and basic knowledge of Turing machine. Methods. By introducing a filter function C(M,w) that is the number of configurations which have more than one children (nondeterministic moves) in the shortest accept computation path of a nondeterministic Turing machine M for input w, for any language L(M) ∈ NP, we can define a series of its subsets, Li(M) = {w | w ∈ L(M) ∧ C(M,w) ≤ i}, and a series of the subsets of NP as Li = {Li(M) | ∀M ∙ L(M) ∈ NP}. The nondeterministic multi-tape Turing machine is used to bridge two language sets Li and Li+1, by simulating the (i+1)-th nondeterministic move deterministically in multiple work tapes, to reduce one (the last) nondeterministic move. Results. The main result is that, with the above methods, the language set Li+1, which seems more powerful, can be proved to be a subset of Li. This result collapses Li ⊆ P for all i ∈ N. With NP = ⋃i∈NLi, it is clear that NP ⊆ P. Because by definition P ⊆ NP, we have P = NP. Discussion. There can be other ways to define the subsets Li and prove the same result. The result can be extended to cover any sets of time functions C, if ∀f ∙ f ∈ C ⇒ f2 ∈ C, then DTIME(C) = NTIME(C). This paper does not show any ways to find a solution in P for the problem known in NP.


Author(s):  
Lance Fortnow

The P versus NP problem is the most important open problem in computer science, if not all of mathematics. Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. This book provides a nontechnical introduction to P versus NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. The book traces the history and development of P versus NP, giving examples from a variety of disciplines, including economics, physics, and biology. It explores problems that capture the full difficulty of the P versus NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The book explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.


Author(s):  
Jia-Bao Liu ◽  
Muhammad Faisal Nadeem ◽  
Mohammad Azeem

Aims and Objective: The idea of partition and resolving sets plays an important role in various areas of engineering, chemistry and computer science such as robot navigation, facility location, pharmaceutical chemistry, combinatorial optimization, networking, and mastermind game. Method: In a graph to obtain the exact location of a required vertex which is unique from all the vertices, several vertices are selected this is called resolving set and its generalization is called resolving partition, where selected vertices are in the form of subsets. Minimum number of partitions of the vertices into sets is called partition dimension. Results: It was proved that determining the partition dimension a graph is nondeterministic polynomial time (NP) problem. In this article, we find the partition dimension of convex polytopes and provide their bounds. Conclusion: The major contribution of this article is that, due to the complexity of computing the exact partition dimension we provides the bounds and show that all the graphs discussed in results have partition dimension either less or equals to 4, but it cannot been be greater than 4.


Author(s):  
Arlindo Oliveira

This chapter covers the development of computing, from its origins, with the analytical engine, to modern computer science. Babbage and Ada Lovelace’s contributions to the science of computing led, in time, to the idea of universal computers, proposed by Alan Turing. These universal computers, proposed by Turing, are conceptual devices that can compute anything that can possibly be computed. The basic concepts created by Turing and Church were further developed to create the edifice of modern computer science and, in particular, the concepts of algorithms, computability, and complexity, covered in this chapter. The chapter ends describing the Church-Turing thesis, which states that anything that can be computed can be computed by a Turing machine.


2019 ◽  
Vol 27 (3) ◽  
pp. 381-439
Author(s):  
Walter Dean

Abstract Computational complexity theory is a subfield of computer science originating in computability theory and the study of algorithms for solving practical mathematical problems. Amongst its aims is classifying problems by their degree of difficulty — i.e., how hard they are to solve computationally. This paper highlights the significance of complexity theory relative to questions traditionally asked by philosophers of mathematics while also attempting to isolate some new ones — e.g., about the notion of feasibility in mathematics, the $\mathbf{P} \neq \mathbf{NP}$ problem and why it has proven hard to resolve, and the role of non-classical modes of computation and proof.


2020 ◽  
Vol 31 (04) ◽  
pp. 527-538
Author(s):  
Grzegorz Madejski ◽  
Andrzej Szepietowski

Two-dimensional general row jumping finite automata were recently introduced as an interesting computational model for accepting two-dimensional languages. These automata are nondeterministic. They guess an order in which rows of the input array are read and they jump to the next row only after reading all symbols in the previous row. In each row, they choose, also nondeterministically, an order in which segments of the row are read. In this paper, we study the membership problem for these automata. We show that each general row jumping finite automaton can be simulated by a nondeterministic Turing machine with space bounded by the logarithm. This means that the fixed membership problems for such automata are in NL, and so in P. On the other hand, we show that the uniform membership problem is NP-complete.


A. Bertoni. Mathematical methods of the theory of stochastic automata. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 9–22. - R. V. Freivald. Functions computable in the limit by probabilistic machines. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 77–87. - B. Goetze and R. Klette. Some properties of limit recursive functions. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 88–90. - Ole-Johan Dahl. An approach to correctness proofs of semicoroutines. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 157–174. - G. Wechsung. The axiomatization problem of a theory of linear languages. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 298–302. - L. Banachowski. Modular approach to the logical theory of programs. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 327–332. - Pierangelo Miglioli. Mathematical foundations of motivation languages and synthesis maps. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, edited by A. Blikle, Lecture notes in computer science, vol. 28, Springer-Verlag, Berlin, Heidelberg, and New York, 1975, pp. 388–408. - H. Rasiowa. ω+-valued algorithmic logic as a tool to investigate procedures. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974, pp. 423–450. - Andrzej Salwicki. Procedures, formal computations and models. Mathematical foundations of computer science, 3rd symposium at Jadwisin near Warsaw, June 17–22, 1974 pp. 464–484.

1977 ◽  
Vol 42 (3) ◽  
pp. 422-423
Author(s):  
Steven S. Muchnick

Author(s):  
Natalya Prokofyeva ◽  
Oksana Zavjalova ◽  
Viktorija Boltunova

The learning process at any stage involves direct interaction between the lecturer and students. The article discusses the lecturer-student relationship as one of the factors that influences the teaching process and improvement of learning materials on the example of the study course “Computer Science”. The study aims at using the results of the survey, as well as student tests as a feedback method to improve the quality of the presentation of new material to first-year students considering the basic knowledge of obtained secondary education. The article discusses two methods of feedback: survey and testing. Survey is considered a method with high efficiency of obtaining information, a possibility of organising mass surveys, an ability to accurately process student survey results. Testing is viewed as a method to identify the level of knowledge and skills, as well as the abilities and other qualities of the educator to meet certain standards by analysing the ways, in which a student performs a number of special tasks. Both methods perfectly complement each other and provide an opportunity to more objectively analyse the learning situation. The article presents the results of the study on the example of the study course “Computer Science” for three academic years, describes changes in the structure of the course, as well as changes in the conduction of practical classes within the course, which improved student performance.


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