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Folds can be regarded as consistently replacing the structural components of a data structure with functions and values. Lists, for example, are built up in many functional languages from two primitives: any list is either an empty list, commonly called nil ([]), or is constructed by prefixing an element in front of another list, creating what is called a cons node ( Cons(X1,Cons(X2,Cons ...
Here, the list [0..] represents , x^2>3 represents the predicate, and 2*x represents the output expression.. List comprehensions give results in a defined order (unlike the members of sets); and list comprehensions may generate the members of a list in order, rather than produce the entirety of the list thus allowing, for example, the previous Haskell definition of the members of an infinite list.
Let A be the sum of the negative values and B the sum of the positive values; the number of different possible sums is at most B-A, so the total runtime is in (()). For example, if all input values are positive and bounded by some constant C, then B is at most N C, so the time required is ().
Let C i (for i between 1 and k) be the sum of subset i in a given partition. Instead of minimizing the objective function max(C i), one can minimize the objective function max(f(C i)), where f is any fixed function. Similarly, one can minimize the objective function sum(f(C i)), or maximize min(f(C i)), or maximize sum(f(C i)).
In number theory and computer science, the partition problem, or number partitioning, [1] is the task of deciding whether a given multiset S of positive integers can be partitioned into two subsets S 1 and S 2 such that the sum of the numbers in S 1 equals the sum of the numbers in S 2. Although the partition problem is NP-complete, there is a ...
Several additional heuristics can be used to improve the runtime: [2] In a node in which the current sum-difference is at least the sum of all remaining numbers, the remaining numbers can just be put in the smallest-sum subset. If we reach a leaf in which the sum-difference is 0 or 1, then the algorithm can terminate since this is the optimum.
The two subsets should contain floor(n/2) and ceiling(n/2) items. It is a variant of the partition problem. It is NP-hard to decide whether there exists a partition in which the sums in the two subsets are equal; see [4] problem [SP12]. There are many algorithms that aim to find a balanced partition in which the sum is as nearly-equal as possible.
Max-sum MSSP: for each subset j in 1,...,m, there is a capacity C j. The goal is to make the sum of all subsets as large as possible, such that the sum in each subset j is at most C j. [1] Max-min MSSP (also called bottleneck MSSP or BMSSP): again each subset has a capacity, but now the goal is to make the smallest subset sum as large as ...