_____________ algorithm is used to make a locally optimal choice in the locally-optimal choice in the hope that this choice will lead to a globally-optimal solutiona.Noneb.Branch and bound c.Divide and conquerd.Greedy

A greedy algorithm is an approach to problem-solving that involves making the locally optimal choice at each stage with the hope of finding a global optimum. The strategy of a greedy algorithm is to pick the best option available at the current moment without worrying about the future consequences.Below are examples of greedy algorithms:1. Fractional Knapsack Problem: Given a set of items with weights and values, the goal is to maximize the total value of items in a knapsack without exceeding its capacity. Greedy approach involves selecting items with the highest value-to-weight ratio first.2. Activity Selection Problem: Given a set of activities with start and finish times, the goal is to select the maximum number of non-overlapping activities. Greedy approach involves sorting the activities by their finish times and selecting the first compatible activity.3. Coin Change Problem: Finding the minimum number of coins needed to make change for a given amount. The greedy approach involves selecting the largest denomination coins as much as possible.4. Job Scheduling: Given a set of jobs with deadlines and profits, the goal is to maximize the total profit by scheduling the jobs within their deadlines. Greedy approach involves sorting the jobs by profit and selecting the highest profit job that doesn’t violate the deadline.Assignment Task:Provide a sample problem with solution of a greedy algorithm.

"A globally-optimal solution can always be found by a series of local improvements from a starting configuration" is a property used by1 pointGreedy AlgorithmDynamic ProgrammingBrute ForceBranch and Bound

Nonlinear optimization problems can have only one local optimal solution.Group of answer choicesTrueFalse

Greedy Algorithms

If an optimal solution can be created for a problem by constructing optimal solutions for its subproblems, the problem possesses ____________ property. Options Overlapping subproblems Optimal substructure Memorization Greedy