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Many mathematical problems have been stated but not yet solved. These problems come from many areas of mathematics, such as theoretical physics, computer science, algebra, analysis, combinatorics, algebraic, differential, discrete and Euclidean geometries, graph theory, group theory, model theory, number theory, set theory, Ramsey theory, dynamical systems, and partial differential equations.
The question is whether or not, for all problems for which an algorithm can verify a given solution quickly (that is, in polynomial time), an algorithm can also find that solution quickly. Since the former describes the class of problems termed NP, while the latter describes P, the question is equivalent to asking whether all problems in NP are ...
The answer to the first question is 2 / 3 , as is shown correctly by the "simple" solutions. But the answer to the second question is now different: the conditional probability the car is behind door 1 or door 2 given the host has opened door 3 (the door on the right) is 1 / 2 .
In this case, the proof shows that a solution of Sudoku in polynomial time could also be used to complete Latin squares in polynomial time. [12] This in turn gives a solution to the problem of partitioning tri-partite graphs into triangles, [ 13 ] which could then be used to find solutions for the special case of SAT known as 3-SAT, [ 14 ...
Dynamic programming is used to solve this problem in the general case by performing the first step and then using the solution of the remaining problem. When the index starts from one, then the person at s {\displaystyle s} shifts from the first person is in position ( ( s − 1 ) mod n ) + 1 {\displaystyle ((s-1){\bmod {n}})+1} , where n is ...
Math on Trial consists of ten chapters, each outlining a particular mathematical fallacy, presenting a case study of a trial in which it arose, and then detailing the effects of the fallacy on the case outcome [1] [2] The cases range over a wide range of years and locations, and are roughly ordered by the sophistication of the reasoning needed to resolve them. [3]
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One important drawback for applications of the solution of the classical secretary problem is that the number of applicants must be known in advance, which is rarely the case. One way to overcome this problem is to suppose that the number of applicants is a random variable N {\displaystyle N} with a known distribution of P ( N = k ) k = 1 , 2 ...
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