Ruthie Newman, CS fundamentals, cohort 15, paper #49
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…meters of left and right added to the max_sub_array method in max_subarray.py.
…ored max_sub_array method.
CheezItMan
reviewed
Mar 11, 2022
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| def newman_conway(num): | ||
| """ Returns a list of the Newman Conway numbers for the given value. | ||
| Time Complexity: ? | ||
| Space Complexity: ? | ||
| Time Complexity: O(n) because the number of calculations performed depends on the size of num. | ||
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| Space Complexity: Space complexity is also O(n) becuase newman_conway_nums array to store sequence values, | ||
| nm_sequence_without_leading_zero to store result with leading 0 removed and result a array to which the properly | ||
| formatted result is saved are created and the amount of space that they occupy will depend on the size of the given num. |
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| def max_sub_array(nums, left=0, right=None): | ||
| """ Returns the max subarray of the given list of numbers. | ||
| Returns 0 if nums is None or an empty list. | ||
| Time Complexity: ? | ||
| Space Complexity: ? | ||
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| Time Complexity: Time complexity is O(nLogn) time because of the implementation of a Divide and Conquer approach, in which the max subarray of | ||
| the given array is found through comparing the left and right value of a recursively calculated mid-point. This method also recursively calls the | ||
| helper method; max_mid_array() which considers max subarrays that range from left to right across the mid-point, with an O(nLogn) time complexity. | ||
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| Space Complexity: Space complexity should be O(nLogn) as well because, according to my research, space complexity is relative to the size of the | ||
| call stack, which decreases by 2 values with each recursive call. | ||
| """ |
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👍 This works, but if you use a dynamic programming approach you can get it done in O(n) time complexity.
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Dynamic Programming