class Solution:
def evalRPN(self, tokens: List[str]) -> int:
"""
Evaluate an arithmetic expression in Reverse Polish Notation (RPN).
Parameters:
tokens (List[str]): A list of strings representing the RPN expression.
Each token is either an integer in string format or one of the four operators: "+", "-", "*", "/".
Returns:
int: The evaluated result of the RPN expression as an integer.
"""
stack = [] # Stack to store operands for the calculations.
# Iterate through each token in the given list.
for token in tokens:
if token in {"+", "-", "*", "/"}:
# Pop the two topmost numbers from the stack.
# 'b' is the most recent operand, 'a' is the previous one.
b = stack.pop()
a = stack.pop()
# Apply the correct operator to the operands.
if token == "+":
result = a + b # Addition
elif token == "-":
result = a - b # Subtraction
elif token == "*":
result = a * b # Multiplication
elif token == "/":
# Perform division that truncates toward zero.
# Using int() on the division result ensures truncation.
result = int(a / b)
# Push the result of the operation back on the stack.
stack.append(result)
else:
# If the token is a number, convert it to int and push it onto the stack.
stack.append(int(token))
# After processing all tokens, the stack should have one element, the final result.
return stack[0]
Summary of Techniques and Approaches:
Stack-Based Evaluation: The core idea uses a stack to store operands and perform operations when encountering an operator. This technique is invaluable for evaluating Reverse Polish Notation and can also be used for syntax parsing, expression evaluation, and backtracking problems.
Token Parsing and Conditional Branching: The solution iterates through a list of tokens and uses conditional logic to differentiate between numbers and operators. This approach is widely applicable in problems where input must be parsed and processed based on type or content, such as interpreters or custom parsers.
Handling Edge Cases in Arithmetic Operations: For division, special care is taken to handle truncation toward zero. This is an example of attention to problem constraints, ensuring that computed results meet given specifications. This careful constraint handling is applicable in any problem where exact arithmetic or rounding rules are specified.
Single-Pass Evaluation: The solution processes the expression in one pass, optimizing both time and space complexity. Recognizing opportunities to solve problems in a single pass is a powerful technique that enhances performance in a variety of algorithmic challenges.
These general techniques can be applied across multiple domains: use stacks for problems with nested structures or reverse processing, branch conditionally when different token types or cases are encountered, and always consider edge cases related to arithmetic operations or input constraints.