Top K Relevant Apps by Keyword Scores

Given a set of apps and per-keyword relevance scores, return the Top K most relevant apps. Input An integer K. A JSON/dictionary apps where each key is an app name (string) and each value is a map keyword -> score (float). Example: { "github": {"diff": 0.3, "code review": 0.8}, "jira": {"ticket": 0.6, "bug": 0.4} } Relevance definition (concrete for implementation) Overall relevance of an app = the maximum score among its keywords. Sort apps by overall relevance descending; break ties by app name lexicographically ascending. Output Return a list of app names of length K (if total apps < K, return all). Constraints 1 <= N <= 500 (number of apps) 1 <= K <= N Each app has at least one keyword score Scores are non-negative floats Test Cases Input: K=2 apps={"github":{"diff":0.3,"code review":0.8},"jira":{"ticket":0.6,"bug":0.4},"notion":{"doc":0.7}} Output: ["github","notion"] Input: K=3 apps={"a":{"k1":1.0},"b":{"k1":1.0},"c":{"k1":0.9}} Output: ["a","b","c"] Input: K=5 apps={"slack":{"m":0.2},"teams":{"m":0.4}} Output: ["teams","slack"] Input: K=1 apps={"x":{"a":0.0,"b":0.0},"y":{"a":0.1}} Output: ["y"] Input: K=2 apps={"z":{"k":0.5},"aa":{"k":0.5},"b":{"k":0.5}} Output: ["aa","b"] Example Input K=2 apps={"github":{"diff":0.3,"code review":0.8},"jira":{"ticket":0.6,"bug":0.4},"notion":{"doc":0.7}} Output ["github","notion"]