- AA technique for designing efficient algorithms by breaking down a problem into smaller subproblems
- BA way to store and organize data in a computer program
- CA process of optimizing memory usage in a program
- DA method for creating algorithms that use only constant space
Dynamic Programming - Quiz
- AOverlapping subproblems
- BOptimal substructure
- CRecursion
- DDivide and conquer
- ASorting a list of integers in ascending order
- BFinding the shortest path between two nodes in a graph
- CComputing the nth Fibonacci number
- DCalculating the greatest common divisor of two numbers
- AThe process of storing solutions to subproblems in memory
- BA way to optimize the use of memory in a program
- CA technique for creating algorithms that use only constant space
- DA method for breaking down a problem into smaller subproblems
- AO(n)
- BO(log n)
- CO(n^2)
- DO(2^n)
- AComputing the nth Fibonacci number
- BFinding the longest common subsequence between two strings
- CFinding the shortest path between two nodes in a graph
- DSorting a list of integers in ascending order
- AA way to store and organize data in a computer program
- BA technique for breaking down a problem into smaller subproblems
- CA process of optimizing memory usage in a program
- D A method of solving problems by starting with the overall problem and recursively breaking it down into smaller subproblems
- AComputing the nth prime number
- BFinding the minimum cost path in a weighted graph
- CSorting a list of strings in lexicographic order
- DComputing the edit distance between two strings
- ADynamic programming involves breaking a problem down into smaller subproblems and reusing solutions to those subproblems, while brute force involves exhaustively searching through all possible solutions.
- BDynamic programming involves solving subproblems independently and combining the results, while brute force involves solving the problem in a step-by-step fashion.
- CDynamic programming involves solving the problem using only constant space, while brute force uses as much space as needed.
- DDynamic programming is faster than brute force for all types of problems.
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Dynamic Programming - Quiz
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- A. A technique for designing efficient algorithms by breaking down a problem into smaller subproblems
- B. A way to store and organize data in a computer program
- C. A process of optimizing memory usage in a program
- D. A method for creating algorithms that use only constant space
Remarks:
Explanation:
Answer: A technique for designing efficient algorithms by breaking down a problem into smaller subproblems
Explanation: Dynamic programming is a technique used in computer science to solve complex problems by breaking them down into smaller, more manageable subproblems. It involves storing solutions to subproblems in memory and reusing them to solve larger problems. This technique can greatly improve the performance of algorithms by reducing redundant computations.
- A. Overlapping subproblems
- B. Optimal substructure
- C. Recursion
- D. Divide and conquer
Remarks:
Explanation:
Answer: Divide and conquer
Explanation: Divide and conquer is a different algorithmic technique that involves breaking a problem into smaller subproblems, solving each subproblem independently, and combining the results. Dynamic programming, on the other hand, involves solving subproblems in a way that allows solutions to be reused to solve larger problems.
- A. Sorting a list of integers in ascending order
- B. Finding the shortest path between two nodes in a graph
- C. Computing the nth Fibonacci number
- D. Calculating the greatest common divisor of two numbers
Remarks:
Explanation:
Answer: Computing the nth Fibonacci number
Explanation: The Fibonacci sequence is a classic example of a problem that can be solved using dynamic programming. The nth Fibonacci number is defined as the sum of the two preceding numbers in the sequence, and computing it involves solving many overlapping subproblems.
- A. The process of storing solutions to subproblems in memory
- B. A way to optimize the use of memory in a program
- C. A technique for creating algorithms that use only constant space
- D. A method for breaking down a problem into smaller subproblems
Remarks:
Explanation:
Answer: The process of storing solutions to subproblems in memory
Explanation: Memoization is a technique used in dynamic programming to speed up the process of solving complex problems by storing solutions to subproblems in memory. This allows the program to avoid redundant computations and greatly improve performance.
- A. O(n)
- B. O(log n)
- C. O(n^2)
- D. O(2^n)
Remarks:
Explanation:
Answer: O(n)
Explanation: The dynamic programming algorithm for computing the nth Fibonacci number has a time complexity of O(n) because it involves solving a linear number of subproblems.
- A. Computing the nth Fibonacci number
- B. Finding the longest common subsequence between two strings
- C. Finding the shortest path between two nodes in a graph
- D. Sorting a list of integers in ascending order
Remarks:
Explanation:
Answer: Computing the nth Fibonacci number
Explanation: The bottom-up approach in dynamic programming involves solving smaller subproblems first and storing their solutions in memory, then using those solutions to solve larger problems. This approach is well-suited to problems like computing the nth Fibonacci number, where the solutions to smaller subproblems can be used to efficiently compute the solution to the overall problem.
- A. A way to store and organize data in a computer program
- B. A technique for breaking down a problem into smaller subproblems
- C. A process of optimizing memory usage in a program
- D. A method of solving problems by starting with the overall problem and recursively breaking it down into smaller subproblems
Remarks:
Explanation:
Answer: A method of solving problems by starting with the overall problem and recursively breaking it down into smaller subproblems
Explanation: The top-down approach in dynamic programming involves starting with the overall problem and recursively breaking it down into smaller subproblems. This approach can be implemented using recursion, and it typically involves storing the solutions to subproblems in memory to avoid redundant computations.
- A. Computing the nth prime number
- B. Finding the minimum cost path in a weighted graph
- C. Sorting a list of strings in lexicographic order
- D. Computing the edit distance between two strings
Remarks:
Explanation:
Answer: Computing the edit distance between two strings
Explanation: Memoization is a technique used in dynamic programming to speed up the process of solving complex problems by storing solutions to subproblems in memory. Problems like computing the edit distance between two strings can benefit from memoization because they involve solving many overlapping subproblems.
- A. O(1)
- B. O(log n)
- C. O(n)
- D. O(n^2)
Remarks:
Explanation:
Answer: O(n)
Explanation: The dynamic programming algorithm for computing the nth Fibonacci number using memoization has a space complexity of O(n) because it involves storing the solutions to n subproblems in memory.
- A. Dynamic programming involves breaking a problem down into smaller subproblems and reusing solutions to those subproblems, while brute force involves exhaustively searching through all possible solutions.
- B. Dynamic programming involves solving subproblems independently and combining the results, while brute force involves solving the problem in a step-by-step fashion.
- C. Dynamic programming involves solving the problem using only constant space, while brute force uses as much space as needed.
- D. Dynamic programming is faster than brute force for all types of problems.
Remarks:
Explanation:
Answer: Dynamic programming involves breaking a problem down into smaller subproblems and reusing solutions to those subproblems, while brute force involves exhaustively searching through all possible solutions.
Explanation: The main difference between dynamic programming and brute force is that dynamic programming involves breaking a problem down into smaller subproblems and reusing solutions to those subproblems, while brute force involves exhaustively searching through all possible solutions. This makes dynamic programming much more efficient than brute force for many types of problems.