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LeetCode LLC, doing business as LeetCode, is an online platform for coding interview preparation. The platform provides coding and algorithmic problems intended for users to practice coding . [ 1 ] LeetCode has gained popularity among job seekers in the software industry and coding enthusiasts as a resource for technical interviews and coding ...
[1] 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.
Given a solution to the SubsetSumPositive instance, adding the −T yields a solution to the SubsetSumZero instance. Conversely, given a solution to the SubsetSumZero instance, it must contain the − T (since all integers in S are positive), so to get a sum of zero, it must also contain a subset of S with a sum of + T , which is a solution of ...
An optimization problem asks for finding a "best possible" solution among the set of all possible solutions to a search problem. One example is the maximum independent set problem: "Given a graph G, find an independent set of G of maximum size." Optimization problems are represented by their objective function and their constraints.
This algorithm may yield a non-optimal solution. For example, suppose there are two tasks and two agents with costs as follows: Alice: Task 1 = 1, Task 2 = 2. George: Task 1 = 5, Task 2 = 8. The greedy algorithm would assign Task 1 to Alice and Task 2 to George, for a total cost of 9; but the reverse assignment has a total cost of 7.
A fractional set cover is an assignment of a fraction (a number in [0,1]) to each set in , such that for each element x in the universe, the sum of fractions of sets that contain x is at least 1. The goal is to find a fractional set cover in which the sum of fractions is as small as possible. Note that a (usual) set cover is equivalent to a ...
Given any positive integer k≥3, the k-set packing problem is a variant of set packing in which each set contains at most k elements. When k =1, the problem is trivial. When k =2, the problem is equivalent to finding a maximum cardinality matching , which can be solved in polynomial time.
A solution for that instance is a bit string that assigns every the value 0 or 1. In this case, a solution consists of 3 bits, for example s = 000 {\displaystyle s=000} , which stands for the assignment of x 1 {\displaystyle x_{1}} to x 3 {\displaystyle x_{3}} with the value 0.