P vs NP: P is Equal to NP: Desired Proof

Zulfia A. Chotchaeva
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Abstract

Computations and computational complexity are fundamental for mathematics and all computer science, including web load time, cryptography (cryptocurrency mining), cybersecurity, artificial intelligence, game theory, multimedia processing, computational physics, biology (for instance, in protein structure prediction), chemistry, and the P vs. NP problem that has been singled out as one of the most challenging open problems in computer science and has great importance as this would essentially solve all the algorithmic problems that we have today if the problem is solved, but the existing complexity is deprecated and does not solve complex computations of tasks that appear in the new digital age as efficiently as it needs. Therefore, we need to realize a new complexity to solve these tasks more rapidly and easily. This paper presents proof of the equality of P and NP complexity classes when the NP problem is not harder to compute than to verify in polynomial time if we forget recursion that takes exponential running time and goes to regress only (every problem in NP can be solved in exponential time, and so it is recursive, this is a key concept that exists, but recursion does not solve the NP problems efficiently). The paper’s goal is to prove the existence of an algorithm solving the NP task in polynomial running time. We get the desired reduction of the exponential problem to the polynomial problem that takes O(log n) complexity.
P vs NP: P等于NP:期望证明
计算和计算复杂性是数学和所有计算机科学的基础,包括web加载时间、密码学(加密货币挖掘)、网络安全、人工智能、博弈论、多媒体处理、计算物理、生物学(例如蛋白质结构预测)、化学、P与NP问题被认为是计算机科学中最具挑战性的开放问题之一,它非常重要,因为如果这个问题得到解决,它将从根本上解决我们今天遇到的所有算法问题,但现有的复杂性被弃用了,并且不能有效地解决新数字时代出现的复杂任务的计算。因此,我们需要认识到一种新的复杂性,以便更快、更容易地解决这些任务。本文给出了P和NP复杂度类相等的证明,当NP问题的计算并不比在多项式时间内验证更难时,如果我们忘记了需要指数运行时间的递归并且只去回归(NP中的每个问题都可以在指数时间内解决,因此它是递归的,这是一个存在的关键概念,但递归不能有效地解决NP问题)。本文的目标是证明在多项式运行时间内解决NP任务的算法的存在性。我们得到了将指数问题简化为多项式问题所需的O(log n)复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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