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An algorithm should have at least one well-defined input.
An algorithm should have at least one well-defined input.
False
An algorithm is considered unambiguous if each of its steps and their inputs/outputs are unclear.
An algorithm is considered unambiguous if each of its steps and their inputs/outputs are unclear.
False
Finiteness in algorithms means that algorithms can continue running indefinitely.
Finiteness in algorithms means that algorithms can continue running indefinitely.
False
Feasibility of an algorithm means that it should be impossible to implement with the available resources.
Feasibility of an algorithm means that it should be impossible to implement with the available resources.
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Algorithm analysis a priori is an empirical analysis of the efficiency of an algorithm.
Algorithm analysis a priori is an empirical analysis of the efficiency of an algorithm.
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Space complexity in algorithms refers to the amount of time required by an algorithm to solve a problem.
Space complexity in algorithms refers to the amount of time required by an algorithm to solve a problem.
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The space complexity of the given algorithm SUM(A, B) is calculated by adding up the space required for each variable and constant.
The space complexity of the given algorithm SUM(A, B) is calculated by adding up the space required for each variable and constant.
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The constant 10 in the algorithm SUM(A, B) contributes 2 units to the space complexity calculation.
The constant 10 in the algorithm SUM(A, B) contributes 2 units to the space complexity calculation.
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In the given algorithm, C ← A + B + 10, the variable C takes up additional space equal to the sum of A and B.
In the given algorithm, C ← A + B + 10, the variable C takes up additional space equal to the sum of A and B.
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Each step in an algorithm should be dependent on specific programming code for clarity.
Each step in an algorithm should be dependent on specific programming code for clarity.
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