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To analyze the time complexity, the video treats each node in the recursion tree as one unit of computation (one function call). The total execution cost of the algorithm is then evaluated by counting the total number of nodes in the tree, including both internal nodes and leaf nodes.

Conditions: The recursive algorithm can be clearly represented by a recursion tree.; The overhead of each function call (excluding recursive calls) is assumed to be constant.

To analyze the time complexity, the video treats each node in the recursion tree as one unit of computation (one function call). The total execution cost of the algorithm is then evaluated by counting the total number of nodes in the tree, including both internal nodes and leaf nodes.

Conditions: The recursive algorithm can be clearly represented by a recursion tree.; The overhead of each function call (excluding recursive calls) is assumed to be constant.

By summing the number of nodes across all levels, the total size of the tree is estimated. The video uses the finite geometric series example $2^0+2^1+\cdots+2^6=2^7-1$ to show that the sum is dominated by the highest-order term.

Conditions: Time cost is measured by the number of recursive calls.; An upper bound estimation using a perfect binary tree is adopted.; The sum starts from $2^0$ with a common ratio of 2.

By summing the number of nodes across all levels, the total size of the tree is estimated. The video uses the finite geometric series example $2^0+2^1+\cdots+2^6=2^7-1$ to show that the sum is dominated by the highest-order term.

Conditions: Time cost is measured by the number of recursive calls.; An upper bound estimation using a perfect binary tree is adopted.; The sum starts from $2^0$ with a common ratio of 2.