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The "diamond problem" (sometimes referred to as the "Deadly Diamond of Death" [6]) is an ambiguity that arises when two classes B and C inherit from A, and class D inherits from both B and C. If there is a method in A that B and C have overridden , and D does not override it, then which version of the method does D inherit: that of B, or that of C?
The Monty Hall problem is a brain teaser, in the form of a probability puzzle, based nominally on the American television game show Let's Make a Deal and named after its original host, Monty Hall. The problem was originally posed (and solved) in a letter by Steve Selvin to the American Statistician in 1975.
Parsons problems consist of a partially completed solution and a selection of lines of code that some of which, when arranged appropriately, correctly complete the solution. There is great flexibility in how Parsons problems can be designed, including the types of code fragments from which to select, and how much structure of the solution is ...
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
See Waring's problem and the related Waring–Goldbach problem on sums of powers of primes. Hardy and Littlewood listed as their Conjecture I: "Every large odd number (n > 5) is the sum of a prime and the double of a prime". [32] This conjecture is known as Lemoine's conjecture and is also called Levy's conjecture.
Other problems for which the greedy algorithm gives a strong guarantee, but not an optimal solution, include Set cover; The Steiner tree problem; Load balancing [11] Independent set; Many of these problems have matching lower bounds; i.e., the greedy algorithm does not perform better than the guarantee in the worst case.
Suppose we have the linear program: Maximize c T x subject to Ax ≤ b, x ≥ 0.. We would like to construct an upper bound on the solution. So we create a linear combination of the constraints, with positive coefficients, such that the coefficients of x in the constraints are at least c T.
The problem of computing the nth Fibonacci number F(n), can be broken down into the subproblems of computing F(n − 1) and F(n − 2), and then adding the two. The subproblem of computing F ( n − 1) can itself be broken down into a subproblem that involves computing F ( n − 2).