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  2. List of NP-complete problems - Wikipedia

    en.wikipedia.org/wiki/List_of_NP-complete_problems

    The problem for graphs is NP-complete if the edge lengths are assumed integers. The problem for points on the plane is NP-complete with the discretized Euclidean metric and rectilinear metric. The problem is known to be NP-hard with the (non-discretized) Euclidean metric. [3]: ND22, ND23

  3. NP-completeness - Wikipedia

    en.wikipedia.org/wiki/NP-completeness

    NP-complete problems are in NP, the set of all decision problems whose solutions can be verified in polynomial time; NP may be equivalently defined as the set of decision problems that can be solved in polynomial time on a non-deterministic Turing machine.

  4. Secretary problem - Wikipedia

    en.wikipedia.org/wiki/Secretary_problem

    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 ...

  5. Archimedes's cattle problem - Wikipedia

    en.wikipedia.org/wiki/Archimedes's_cattle_problem

    Archimedes's cattle problem (or the problema bovinum or problema Archimedis) is a problem in Diophantine analysis, the study of polynomial equations with integer solutions. Attributed to Archimedes , the problem involves computing the number of cattle in a herd of the sun god from a given set of restrictions.

  6. P versus NP problem - Wikipedia

    en.wikipedia.org/wiki/P_versus_NP_problem

    Clearly, a #P problem must be at least as hard as the corresponding NP problem, since a count of solutions immediately tells if at least one solution exists, if the count is greater than zero. Surprisingly, some #P problems that are believed to be difficult correspond to easy (for example linear-time) P problems. [ 18 ]

  7. Problem statement - Wikipedia

    en.wikipedia.org/wiki/Problem_statement

    There are several basic elements that can be built into every problem statement. The problem statement should focus on the end user, and the statement should not be too broad or narrow. [7] Problem statements usually follow a format. While there are several options, the following is a template often used in business analysis.

  8. Uncertainty quantification - Wikipedia

    en.wikipedia.org/wiki/Uncertainty_quantification

    Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.

  9. Hilbert's problems - Wikipedia

    en.wikipedia.org/wiki/Hilbert's_problems

    Problems 1, 2, 5, 6, [g] 9, 11, 12, 15, 21, and 22 have solutions that have partial acceptance, but there exists some controversy as to whether they resolve the problems. That leaves 8 (the Riemann hypothesis ), 13 and 16 [ h ] unresolved, and 4 and 23 as too vague to ever be described as solved.