An efﬁcient decoding algorithm is a crucial element of any statistical machine translation system. Some researchers have noted certain similarities between SMT decoding and the famous Traveling Salesman Problem; in particular (Knight, 1999) has shown that any TSP instance can be mapped to a sub-case of a word-based SMT model, demonstrating NP-hardness of the decoding task. In this paper, we focus on the reverse mapping, showing that any phrase-based SMT decoding problem can be directly reformulated as a TSP.
This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented.
In this paper we consider a combinatorial optimization problem that is similar to the bottleneck traveling salesman problem. We show that an optimal tour for this problem is pyramidal tour (1, 3, 5,…, n,…, 6, 4, 2) or consists of some pyramidal subtours. The above 7methods can be extended to complete bipartite graphs. 1.
It is well-known that the traveling salesman problem (TSP) is strongly NP-hard (cf. , p. 353). But for some special cases of the TSP can be solvable in polynomial time. ...
In the middle 1930s computer science was yet a not well defined academic discipline.
Actually, fundamental concepts, such as ‘algorithm’, or ‘computational problem’, has been
formalized just some year before.
In these years the Austrian mathematician Karl Menger invited the research community
to consider from a mathematical point of view the following problem taken from the every
day life. A traveling salesman has to visit exactly once each one of a list of m cities and then
return to the home city. He knows the cost of traveling from any city i to any other city j.
Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems....
Phương pháp tối ưu hóa đàn kiến (Ant Colony Optimization – ACO) là một phương pháp mới mà ngày nay người ta rất quan tâm vì những hiệu quả nổi trội của nó so với các phuoeng pháp khác trong giải quyết các bài toán tối ưu hóa tổ hợp (Combinatorial optimization problems).
Salesman: As you see, when we try to clean even the dirtiest part of your carpet there is no problem for this vacuum-cleaner.
Customer: But how easily can I learn to operate this machine? It seems to be extremely complicated to me.
Salesman: If you want to know how to get the best out of your new Dirt-Up vacuum cleaner you should read this brochure. It tells you everything you need to know.
read in bed.
I cannot bear him to read in bed.
/ know him to be an honest man
We can use that or to-infinitive after these verbs to refer to people...