Dynamic Programming combines the principles of breaking down problems into subproblems, using recursion, and storing solutions to avoid recomputation.

The Fibonacci Sequence, Longest Common Subsequence, and Traveling Salesman Problem are all well-known examples of problems that can be efficiently solved using Dynamic Programming.

Using Dynamic Programming, the Fibonacci Sequence can be solved in linear time, significantly improving the efficiency compared to the recursive approach.

A memoization table is a key component of Dynamic Programming. It stores the solutions to subproblems to avoid recomputation, thereby improving the efficiency of the algorithm.

The principle of optimality is a fundamental concept in Dynamic Programming. It states that an optimal solution to a problem can be constructed from optimal solutions to its subproblems.

Divide and Conquer is a widely used technique in Dynamic Programming. It involves breaking down a problem into smaller subproblems, solving them recursively, and combining the solutions to obtain the final solution.

The primary advantage of Dynamic Programming lies in its ability to avoid redundant computations by storing solutions to subproblems. This significantly improves the efficiency of the algorithm, especially for problems with overlapping subproblems.

Dijkstra's Algorithm is a greedy algorithm primarily used for solving shortest path problems. Unlike other problems mentioned, it does not exhibit the optimal substructure property, which is a key requirement for applying Dynamic Programming.

The Dynamic Programming solution for the Longest Common Subsequence problem has a time complexity of O(n^2), where n is the length of the input sequences.

The overlapping subproblems property refers to the situation where a problem's subproblems share common solutions. This property is crucial for applying Dynamic Programming, as it allows for the storage and reuse of solutions to avoid redundant computations.

The Traveling Salesman Problem is an example where the optimal solution cannot always be constructed from optimal solutions to its subproblems. This is because the optimal solution may require visiting cities in a specific order, which may not be evident from the optimal solutions to the subproblems.

The bottom-up approach in Dynamic Programming involves starting from the smallest subproblems and gradually building up to the larger ones. This ensures that the solutions to the smaller subproblems are available when solving the larger ones, avoiding redundant computations.

Edit Distance is a classic Dynamic Programming problem that involves finding the minimum number of operations (insertions, deletions, or substitutions) required to transform one string into another.

The Dynamic Programming solution for the Knapsack Problem has a time complexity of O(nW), where n is the number of items and W is the maximum capacity of the knapsack.

The Longest Common Subsequence problem is an example where the optimal solution can be constructed from optimal solutions to its subproblems. The optimal solution to the entire problem can be obtained by combining the optimal solutions to its smaller subproblems.