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In computer programming, a name collision is the nomenclature problem that occurs when the same variable name is used for different things in two separate areas that are joined, merged, or otherwise go from occupying separate namespaces to sharing one.
Intuitively, if we have a procedure for the search problem, the decision version can be solved easily: just feed the input samples for the decision problem to the solver for the search problem. Denote the given samples by { ( a i , b i ) } ⊂ Z q n × T {\displaystyle \{(\mathbf {a} _{i},\mathbf {b} _{i})\}\subset \mathbb {Z} _{q}^{n}\times ...
Although this problem seems easier, Valiant and Vazirani have shown [25] that if there is a practical (i.e. randomized polynomial-time) algorithm to solve it, then all problems in NP can be solved just as easily. MAX-SAT, the maximum satisfiability problem, is an FNP generalization of SAT. It asks for the maximum number of clauses which can be ...
Although this will not work in all cases, for example when calling the wrong subroutine, it is the easiest way to find the problem if the program uses the incorrect results of a bad mathematical calculation.
Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods.
For example, in the factoring problem, the instances are the integers n, and solutions are prime numbers p that are the nontrivial prime factors of n. An example of a computational problem without a solution is the Halting problem. Computational problems are one of the main objects of study in theoretical computer science.
This is an unbalanced assignment problem. One way to solve it is to invent a fourth dummy task, perhaps called "sitting still doing nothing", with a cost of 0 for the taxi assigned to it. This reduces the problem to a balanced assignment problem, which can then be solved in the usual way and still give the best solution to the problem.
A polynomial-time problem can be very difficult to solve in practice if the polynomial's degree or constants are large enough. In addition, information-theoretic security provides cryptographic methods that cannot be broken even with unlimited computing power. "A large-scale quantum computer would be able to efficiently solve NP-complete problems."