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Greedy algorithms fail to produce the optimal solution for many other problems and may even produce the unique worst possible solution. One example is the travelling salesman problem mentioned above: for each number of cities, there is an assignment of distances between the cities for which the nearest-neighbour heuristic produces the unique ...
For example, the largest amount that cannot be obtained using only coins of 3 and 5 units is 7 units. The solution to this problem for a given set of coin denominations is called the Frobenius number of the set. The Frobenius number exists as long as the set of coin denominations is setwise coprime.
The Millionaires' problem is an important problem in cryptography, the solution of which is used in e-commerce and data mining. Commercial applications sometimes have to compare numbers that are confidential and whose security is important. Many solutions have been introduced for the problem, including physical solutions based on cards. [1]
By Jill Krasny and Zachry Floro Math class may have seemed pointless back in the day, but it turns out all those confusing equations are quite useful. Math can be used to solve every money problem ...
The following is a dynamic programming implementation (with Python 3) which uses a matrix to keep track of the optimal solutions to sub-problems, and returns the minimum number of coins, or "Infinity" if there is no way to make change with the coins given. A second matrix may be used to obtain the set of coins for the optimal solution.
The problem concerns two envelopes, each containing an unknown amount of money. The two envelopes problem, also known as the exchange paradox, is a paradox in probability theory. It is of special interest in decision theory and for the Bayesian interpretation of probability theory. It is a variant of an older problem known as the necktie paradox.
Ramsey explained through the call that she is focusing on the wrong money problem. Her joint household income is $125,000 a year, which Dave explains allows her to self-insure her pets.
The question is whether or not, for all problems for which an algorithm can verify a given solution quickly (that is, in polynomial time), an algorithm can also find that solution quickly. Since the former describes the class of problems termed NP, while the latter describes P, the question is equivalent to asking whether all problems in NP are ...
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