+ We've encountered an unexpected error. Please try again. +
+ +
+ {children}
+
+ );
+ },
+
+ code({ className, children, ...props }: React.HTMLAttributes
+ {children}
+
+ );
+ }
+
+ return (
+
+ {children}
+
+ );
+ },
+
+ table({ children, ...props }: React.HTMLAttributes+ {children} ++ ); + }, + + h1({ children, ...props }: React.HTMLAttributes
+ {children} +
+ ); + }, + + strong({ children, ...props }: React.HTMLAttributes{children};
- }
-
- return (
-
- {children}
-
- );
- },
-
- table({ children }) {
- return (
- - {children} -- ); - }, - - h1({ children }) { - return ( -
- {children} -
- ); - }, - - strong({ children }) { - return ( - - {children} - - ); - }, - - em({ children }) { - return {children}; - }, - }} + components={markdownComponents} > {content}+ Current Topic +
+ ++ Big O notation describes how an algorithm's execution time + grows as input size increases. For example, Linear Search has + O(n) complexity because it may need to check every element in + the list. +
++ O(log n) grows much slower than O(n). Binary Search uses O(log + n) because it repeatedly halves the search space, making it + significantly faster for large datasets. +
+Follow-up Questions
+ +3 / 3
+