L-5.3: 0/1 Knapsack Problem |Dynamic Programming |Recursive Equation |Recursion Tree Time Complexity

An equation that defines a problem in terms of smaller instances of the same problem.

A problem where objects with certain weights and profits need to be selected to maximize the total profit, while keeping the total weight within a given limit.

A method that considers all possible combinations of objects to find the optimal solution.

The measure of the amount of time an algorithm takes to run, often expressed in terms of the input size.

An approach to problem-solving that breaks down a problem into smaller overlapping subproblems and solves them in a bottom-up manner to avoid redundant calculations.

A condition that determines when a recursive function should stop and return a result.

A visual representation of the recursive calls made in a recursive algorithm, showing the hierarchy of function calls and their relationships.

The best possible solution or output for a given problem.

A data structure used in dynamic programming to store the values of subproblems for efficient retrieval.

The dimensions of the table used in dynamic programming, determined by the number of objects and the total weight of the knapsack.

The process of determining the time complexity of an algorithm, often by analyzing the number of operations performed as a function of the input size.

The measure of the amount of memory an algorithm requires to run, often expressed in terms of the input size.

The advantages of using dynamic programming, such as improved time efficiency and avoiding redundant calculations.