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For example, for the array of values [−2, 1, −3, 4, −1, 2, 1, −5, 4], the contiguous subarray with the largest sum is [4, −1, 2, 1], with sum 6. Some properties of this problem are: If the array contains all non-negative numbers, then the problem is trivial; a maximum subarray is the entire array.
In the array containing the E(x, y) values, we then choose the minimal value in the last row, let it be E(x 2, y 2), and follow the path of computation backwards, back to the row number 0. If the field we arrived at was E(0, y 1), then T[y 1 + 1] ... T[y 2] is a substring of T with the minimal edit distance to the pattern P.
Intuitively, partial function application says "if you fix the first arguments of the function, you get a function of the remaining arguments". For example, if function div(x,y) = x/y, then div with the parameter x fixed at 1 is another function: div 1 (y) = div(1,y) = 1/y.
Currying is related to, but not the same as, partial application. [1] [2] The example above can be used to illustrate partial application; it is quite similar. Partial application is the function that takes the pair and together as arguments, and returns .
The formula for an integration by parts is () ′ = [() ()] ′ ().. Beside the boundary conditions, we notice that the first integral contains two multiplied functions, one which is integrated in the final integral (′ becomes ) and one which is differentiated (becomes ′).
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
The assignment problem consists of finding, in a weighted bipartite graph, a matching of maximum size, in which the sum of weights of the edges is minimum. If the numbers of agents and tasks are equal, then the problem is called balanced assignment, and the graph-theoretic version is called minimum-cost perfect matching.
The relationship between these two value functions is called the "Bellman equation". In this approach, the optimal policy in the last time period is specified in advance as a function of the state variable's value at that time, and the resulting optimal value of the objective function is thus expressed in terms of that value of the state variable.