This **crossword clue** is for the definition: *“M*A*S*H” role.*

it’s A 26 letters **crossword puzzle definition**.

Next time, when searching for online help with your puzzle, try using the search term ““M*A*S*H” role crossword” or ““M*A*S*H” role crossword clue”. The possible answerss for “M*A*S*H” role are listed below.

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## Possible Answers: **RADAR**.

Last seen on: LA Times Crossword 29 Dec 18, Saturday

### Random information on the term ““M*A*S*H” role”:

E (named e /iː/, plural ees)[1] is the fifth letter and the second vowel in the modern English alphabet and the ISO basic Latin alphabet. It is the most commonly used letter in many languages, including Czech, Danish, Dutch, English, French, German, Hungarian, Latin, Latvian, Norwegian, Spanish, and Swedish.[2][3][4][5][6]

The Latin letter ‘E’ differs little from its source, the Greek letter epsilon, ‘Ε’. This in turn comes from the Semitic letter hê, which has been suggested to have started as a praying or calling human figure (hillul ‘jubilation’), and was probably based on a similar Egyptian hieroglyph that indicated a different pronunciation. In Semitic, the letter represented /h/ (and /e/ in foreign words); in Greek, hê became the letter epsilon, used to represent /e/. The various forms of the Old Italic script and the Latin alphabet followed this usage.

Although Middle English spelling used ⟨e⟩ to represent long and short /e/, the Great Vowel Shift changed long /eː/ (as in ‘me’ or ‘bee’) to /iː/ while short /ɛ/ (as in ‘met’ or ‘bed’) remained a mid vowel. In other cases, the letter is silent, generally at the end of words.

### Random information on the term “RADAR”:

The nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem. In it, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one.

These are the steps of the algorithm:

The sequence of the visited vertices is the output of the algorithm.

The nearest neighbour algorithm is easy to implement and executes quickly, but it can sometimes miss shorter routes which are easily noticed with human insight, due to its “greedy” nature. As a general guide, if the last few stages of the tour are comparable in length to the first stages, then the tour is reasonable; if they are much greater, then it is likely that much better tours exist. Another check is to use an algorithm such as the lower bound algorithm to estimate if this tour is good enough.

In the worst case, the algorithm results in a tour that is much longer than the optimal tour. To be precise, for every constant r there is an instance of the traveling salesman problem such that the length of the tour computed by the nearest neighbour algorithm is greater than r times the length of the optimal tour. Moreover, for each number of cities there is an assignment of distances between the cities for which the nearest neighbor heuristic produces the unique worst possible tour. (If the algorithm is applied on every vertex as the starting vertex, the best path found will be better than at least N/2-1 other tours, where N is the number of vertexes)[1]