Lexicographically Smallest Beautiful String

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class Solution:
    def smallestBeautifulString(self, s: str, k: int) -> str:
        n = len(s)
        s = list(s)

        def valid(i):
            return (i < 1 or s[i] != s[i - 1]) and (i < 2 or s[i] != s[i - 2])

        for i in range(n - 1, -1, -1):
            s[i] = chr(ord(s[i]) + 1)
            while not valid(i):
                s[i] = chr(ord(s[i]) + 1)
            if ord(s[i]) < ord('a') + k:
                for i in range(i + 1, n):
                    s[i] = 'a'
                    while not valid(i):
                        s[i] = chr(ord(s[i]) + 1)
                return ''.join(s)

        return ""

Identifying Problem Isomorphism

“Lexicographically Smallest Beautiful String” is approximately isomorphic to “Permutation Sequence”.

Reasoning:

Both problems deal with finding a specific sequence given certain conditions. In “Lexicographically Smallest Beautiful String”, you’re asked to find the lexicographically smallest “beautiful” string after reordering the given characters. In “Permutation Sequence”, you have to find the k-th permutation sequence of the numbers from 1 to n. Both problems require a deep understanding of permutations and sequences.

“Next Permutation” is another similar problem.

Reasoning:

This problem requires finding the next greater permutation of numbers. It shares a structural similarity with “Lexicographically Smallest Beautiful String” in that it involves finding a sequence that satisfies certain conditions.

Based on simplicity, the order is:

Next Permutation
Lexicographically Smallest Beautiful String
Permutation Sequence

“Next Permutation” is simpler as it only requires finding the next greater permutation. “Lexicographically Smallest Beautiful String” is more complex because it requires finding the smallest “beautiful” string, a task which involves both permutation and some condition-checking logic. “Permutation Sequence” is the most complex, as it involves finding the k-th permutation in a sequence, a task that requires both permutation and counting.

10 Prerequisite LeetCode Problems

“2663. Lexicographically Smallest Beautiful String” likely involves concepts such as depth-first search (DFS), backtracking, and handling lexicographic order. Here are 10 LeetCode problems that can serve as solid practice and preparation:

LeetCode 78. Subsets
- This problem introduces the basics of using depth-first search (DFS) to generate combinations.
LeetCode 22. Generate Parentheses
- A good problem to understand the use of DFS and recursion to generate all valid combinations of a string.
LeetCode 46. Permutations
- This problem is good practice for understanding permutations of a given set of elements, which may be relevant to generating beautiful strings.
LeetCode 47. Permutations II
- An extension of the previous problem, it takes into account duplicate values and can help refine your approach to generating permutations.
LeetCode 31. Next Permutation
- This problem involves generating permutations in lexicographic order, which is a key part of the problem statement.
LeetCode 79. Word Search
- This problem helps you understand how to use DFS to explore all possible paths in a grid.
LeetCode 93. Restore IP Addresses
- This problem requires you to generate all valid combinations of a string which can help you understand how to handle string manipulations along with DFS and backtracking.
LeetCode 17. Letter Combinations of a Phone Number
- This problem is about generating all possible strings from a given input which can help you in understanding DFS and backtracking on strings.
LeetCode 39. Combination Sum
- This problem involves searching for all valid combinations that sum to a target, providing additional practice in DFS and backtracking.
LeetCode 90. Subsets II
- A good problem for understanding backtracking and dealing with duplicate subsets.

These cover depth-first search, backtracking, and handling string permutations, which are likely key components for solving the problem “2663. Lexicographically Smallest Beautiful String”.

Problem Classification

Problem Statement:A string is beautiful if:

It consists of the first k letters of the English lowercase alphabet. It does not contain any substring of length 2 or more which is a palindrome. You are given a beautiful string s of length n and a positive integer k.

Return the lexicographically smallest string of length n, which is larger than s and is beautiful. If there is no such string, return an empty string.

A string a is lexicographically larger than a string b (of the same length) if in the first position where a and b differ, a has a character strictly larger than the corresponding character in b.

For example, “abcd” is lexicographically larger than “abcc” because the first position they differ is at the fourth character, and d is greater than c.

Example 1:

Input: s = “abcz”, k = 26 Output: “abda” Explanation: The string “abda” is beautiful and lexicographically larger than the string “abcz”. It can be proven that there is no string that is lexicographically larger than the string “abcz”, beautiful, and lexicographically smaller than the string “abda”.

Example 2:

Input: s = “dc”, k = 4 Output: "" Explanation: It can be proven that there is no string that is lexicographically larger than the string “dc” and is beautiful.

Constraints:

1 <= n == s.length <= 105 4 <= k <= 26 s is a beautiful string.

Analyze the provided problem statement. Categorize it based on its domain, ignoring ‘How’ it might be solved. Identify and list out the ‘What’ components. Based on these, further classify the problem. Explain your categorizations.

Visual Model of the Problem

How to visualize the problem statement for this problem?

Problem Restatement

Could you start by paraphrasing the problem statement in your own words? Try to distill the problem into its essential elements and make sure to clarify the requirements and constraints. This exercise should aid in understanding the problem better and aligning our thought process before jumping into solving it.

Abstract Representation of the Problem

Could you help me formulate an abstract representation of this problem?

Given this problem, how can we describe it in an abstract way that emphasizes the structure and key elements, without the specific real-world details?

Terminology

Are there any specialized terms, jargon, or technical concepts that are crucial to understanding this problem or solution? Could you define them and explain their role within the context of this problem?

Problem Simplification and Explanation

Could you please break down this problem into simpler terms? What are the key concepts involved and how do they interact? Can you also provide a metaphor or analogy to help me understand the problem better?

Constraints

Given the problem statement and the constraints provided, identify specific characteristics or conditions that can be exploited to our advantage in finding an efficient solution. Look for patterns or specific numerical ranges that could be useful in manipulating or interpreting the data.

What are the key insights from analyzing the constraints?

Case Analysis

Could you please provide additional examples or test cases that cover a wider range of the input space, including edge and boundary conditions? In doing so, could you also analyze each example to highlight different aspects of the problem, key constraints and potential pitfalls, as well as the reasoning behind the expected output for each case? This should help in generating key insights about the problem and ensuring the solution is robust and handles all possible scenarios.

Identification of Applicable Theoretical Concepts

Can you identify any mathematical or algorithmic concepts or properties that can be applied to simplify the problem or make it more manageable? Think about the nature of the operations or manipulations required by the problem statement. Are there existing theories, metrics, or methodologies in mathematics, computer science, or related fields that can be applied to calculate, measure, or perform these operations more effectively or efficiently?

Problem Breakdown and Solution Methodology

Given the problem statement, can you explain in detail how you would approach solving it? Please break down the process into smaller steps, illustrating how each step contributes to the overall solution. If applicable, consider using metaphors, analogies, or visual representations to make your explanation more intuitive. After explaining the process, can you also discuss how specific operations or changes in the problem’s parameters would affect the solution? Lastly, demonstrate the workings of your approach using one or more example cases.

Inference of Problem-Solving Approach from the Problem Statement

How did you infer from the problem statement that this problem can be solved using ?

Could you please provide a stepwise refinement of our approach to solving this problem?
How can we take the high-level solution approach and distill it into more granular, actionable steps?
Could you identify any parts of the problem that can be solved independently?
Are there any repeatable patterns within our solution?

Solution Approach and Analysis

Thought Process

Explain the thought process by thinking step by step to solve this problem from the problem statement and code the final solution. Write code in Python3. What are the cues in the problem statement? What direction does it suggest in the approach to the problem? Generate insights about the problem statement.

From Brute Force to Optimal Solution

Could you please begin by illustrating a brute force solution for this problem? After detailing and discussing the inefficiencies of the brute force approach, could you then guide us through the process of optimizing this solution? Please explain each step towards optimization, discussing the reasoning behind each decision made, and how it improves upon the previous solution. Also, could you show how these optimizations impact the time and space complexity of our solution?

Coding Constructs

Consider the following piece of complex software code.

What are the high-level problem-solving strategies or techniques being used by this code?
If you had to explain the purpose of this code to a non-programmer, what would you say?
Can you identify the logical elements or constructs used in this code, independent of any programming language?
Could you describe the algorithmic approach used by this code in plain English?
What are the key steps or operations this code is performing on the input data, and why?
Can you identify the algorithmic patterns or strategies used by this code, irrespective of the specific programming language syntax?

Language Agnostic Coding Drills

Your mission is to deconstruct this code into the smallest possible learning units, each corresponding to a separate coding concept. Consider these concepts as unique coding drills that can be individually implemented and later assembled into the final solution.

Dissect the code and identify each distinct concept it contains. Remember, this process should be language-agnostic and generally applicable to most modern programming languages.
Once you’ve identified these coding concepts or drills, list them out in order of increasing difficulty. Provide a brief description of each concept and why it is classified at its particular difficulty level.
Next, describe the problem-solving approach that would lead from the problem statement to the final solution. Think about how each of these coding drills contributes to the overall solution. Elucidate the step-by-step process involved in using these drills to solve the problem. Please refrain from writing any actual code; we’re focusing on understanding the process and strategy.

Targeted Drills in Python

Now that you’ve identified and ordered the coding concepts from a complex software code in the previous exercise, let’s focus on creating Python-based coding drills for each of those concepts.

Begin by writing a separate piece of Python code that encapsulates each identified concept. These individual drills should illustrate how to implement each concept in Python. Please ensure that these are suitable even for those with a basic understanding of Python.
In addition to the general concepts, identify and write coding drills for any problem-specific concepts that might be needed to create a solution. Describe why these drills are essential for our problem.
Once all drills have been coded, describe how these pieces can be integrated together in the right order to solve the initial problem. Each drill should contribute to building up to the final solution.

Remember, the goal is to not only to write these drills but also to ensure that they can be cohesively assembled into one comprehensive solution.