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It can also be viewed as an assertion that the real line does not contain any set resembling a finite subring or finite subfield; it is the first example of what is now known as the sum-product phenomenon, which is now known to hold in a wide variety of rings and fields, including finite fields.
The subset sum problem (SSP) is a decision problem in computer science.In its most general formulation, there is a multiset of integers and a target-sum , and the question is to decide whether any subset of the integers sum to precisely . [1]
The multiple subset sum problem is an optimization problem in computer science and operations research. It is a generalization of the subset sum problem . The input to the problem is a multiset S {\displaystyle S} of n integers and a positive integer m representing the number of subsets.
A sum-product number in a given number base is a natural number that is equal to the product of the sum of its digits and the product of its digits. There are a finite number of sum-product numbers in any given base b {\displaystyle b} .
Soot provides four intermediate representations for use through its API for other analysis programs to access and build upon: [2] Baf: a near bytecode representation. Jimple: a simplified version of Java source code that has a maximum of three components per statement. Shimple: an SSA variation of Jimple (similar to GIMPLE).
The product of those two numbers would then be a semiprime. The following steps give the solution: S (Sue), P (Pete), and O (Otto) make tables of all products that can be formed from 2-splits of the sums in the range, i.e. from 5 to 100 (X > 1 and Y > X requires us to start at 5). For example, 11 can be 2-split into 2+9, 3+8, 4+7, and 5+6.
If an element lies in both, there will be two effectively distinct copies of the value in A + B, one from A and one from B. In type theory, a tagged union is called a sum type. Sum types are the dual of product types. Notations vary, but usually the sum type A + B comes with two introduction forms inj 1: A → A + B and inj 2: B → A + B.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.