几种常见时间复杂度分析.html

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    <script>
        // O(1)
        // 总结一下，只要代码的执行时间不随 n 的增大而增长，这样代码的时间复杂度我们都记作 O(1),，只要算法中不存在循环语句、递归语句，即使有成千上万行的代码，其时间复杂度也是Ο(1)
        let num1 = 1;
        let num2 = 2;
        let num3 = 3;
        // O ( logn ) / O ( nlogn )
        let num4 = 1,
            i = 1,
            n = 10;
        while (i <= n) {
            i = i * 3;
        }
        console.log('O(log 3底数 n');
        for (; i <= n; ++i) {
            while (i <= n) {
                i = i * 3;
            }
        }
        console.log('O(n log 3底数 n)');
        // O(m+n) / O(m*n)
        const cal = (m,n) => {
            let sum_1 = 0;
            let i = 1;
            for (; i < m; ++i) {
                sum_1 = sum_1 + i;
            }

            let sum_2 = 0;
            let j = 1;
            for (; j < n; ++j) {
                sum_2 = sum_2 + j;
            }

            return sum_1 + sum_2;
        }
        const m = 1;
        const n1 = 1;

        // 公式  
        // 加法 T1(m) + T2(n) = O(f(m) + g(n))
        console.log('加法：' + (parseInt(m) + parseInt(n1)) )
        // 乘法 T1(m) * T2(n) = O(f(m) * g(n))
        console.log('乘法' + parseInt(m) * parseInt(n1) )
    </script>
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