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On2 vs Onlogn lengthOfLIS
(version: 0)
Comparing performance of:
lengthOfLIS 1 vs lengthOfLIS 2
Created:
5 years ago
by:
Guest
Jump to the latest result
HTML Preparation code:
<script> Benchmark.prototype.setup = function() { const nums = [10, 22, 9, 33, 21, 50, 41, 60, 22, 68, 90]; }; </script>
Tests:
lengthOfLIS 1
function lengthOfLIS1(nums) { let dp = [ [nums[0]] ]; for(let i of nums) { let f = false; // for every entry in the dynamic array check if i > its last element for(let j of dp) { if(i <= j[j.length-1]) { f = true; j[j.length-1] = i; break; } } if(!f) dp.push(dp[dp.length-1].concat([i])); } return nums.length > 0 ? dp[dp.length-1].length : 0; }; lengthOfLIS1(nums)
lengthOfLIS 2
function lengthOfLIS2(nums) { var lis = []; for (var i = 0; i < nums.length; i++) { lis.push(1); for (var j = 0; j < i; j++) { if (nums[j] < nums[i]) lis[i] = Math.max(lis[i], lis[j] + 1); } } return nums.length ? Math.max.apply(null, lis) : 0; } lengthOfLIS2(nums)
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
lengthOfLIS 1
lengthOfLIS 2
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's dive into the benchmark being measured on MeasureThat.net. **Benchmark Overview** The benchmark is designed to compare the performance of two different approaches for finding the length of a Longest Increasing Subsequence (LIS) in an array of numbers. The LIS problem is a classic problem in computer science, where you need to find the longest subsequence within the given array such that all elements in the subsequence are sorted in increasing order. **Options Compared** There are two approaches being compared: 1. **Dynamic Programming Approach (DP)**: This approach uses a dynamic programming technique to build a table of lengths for each element in the array. The table is then used to find the maximum length of an increasing subsequence. 2. **Brute Force Approach**: This approach iterates through all possible subsequences of the input array and checks if they are increasing. If a subsequence is found, its length is updated accordingly. **Pros and Cons** 1. **Dynamic Programming Approach (DP)**: * Pros: More efficient in terms of time complexity (O(n^2) vs O(n log n)), uses less memory. * Cons: Can be slower for smaller input sizes due to overhead of table construction, may not perform well for arrays with a large number of duplicates. 2. **Brute Force Approach**: * Pros: Simple to implement, can handle arrays with a large number of duplicates without significant performance degradation. * Cons: Time complexity is O(n^2) which can be slow for larger input sizes, uses more memory. **Library and Special JS Feature** There is no specific library being used in this benchmark. However, the `lengthOfLIS1` function does use a JavaScript feature called `let` with block scope, which was introduced in ECMAScript 2015 (ES6). This allows for variable declaration without the need for the `var` keyword. **Other Alternatives** For finding the length of an LIS, there are other approaches that could be used: * **Recursive Approach**: This approach would involve writing a recursive function to build the table of lengths. * **Memoization**: This approach involves storing the results of expensive function calls and reusing them when the same inputs occur again. * **Greedy Algorithm**: This approach would involve selecting the largest element in the array at each step, similar to the brute force approach. However, it's worth noting that these alternatives may not be as efficient or effective as the dynamic programming approach for large input sizes.
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