Build Array Where You Can Find The Maximum Exactly K Comparisons

class Solution:
    def numOfArrays(self, n: int, m: int, k: int) -> int:
        def dp(i, h, k):      
            # Base Cases
            if i == n and k == 0:
                return 1
            if i == n or k < 0:
                return 0

            # Recursive Call and Summation
            total_ways = 0
            for j in range(1, m + 1):
                total_ways += dp(i + 1, max(h, j), k - (j > h))
            return total_ways % 1000000007

        # Final Return Statement
        return dp(0, -1, k)

Identifying Problem Isomorphism

“Build Array Where You Can Find The Maximum Exactly K Comparisons” can be mapped to “Count Ways to Build Rooms in an Ant Colony”.

“Build Array Where You Can Find The Maximum Exactly K Comparisons” involves dynamic programming and combinatorics. It’s about finding the number of ways to build an array of a certain length, under specific constraints related to comparisons of maximum elements.

Similarly, “Count Ways to Build Rooms in an Ant Colony” also involves dynamic programming and combinatorics. In this case, the task is to calculate the number of ways to build rooms, given a specific structure of the ant colony, which is represented as a tree.

Both require a deep understanding of combinatorics and dynamic programming to calculate the number of ways to build a structure (an array or rooms) under given constraints. “Count Ways to Build Rooms in an Ant Colony” is more complex due to the additional challenge of dealing with tree data structure.

10 Prerequisite LeetCode Problems

This involves dynamic programming, arrays, and combinatorial mathematics. Here are ten problems to prepare:

  1. 91. Decode Ways
  2. 62. Unique Paths
  3. 96. Unique Binary Search Trees
  4. 139. Word Break
  5. 322. Coin Change
  6. 300. Longest Increasing Subsequence
  7. 518. Coin Change 2
  8. 646. Maximum Length of Pair Chain
  9. 673. Number of Longest Increasing Subsequence
  10. 688. Knight Probability in Chessboard

These involve similar techniques. They require understanding of dynamic programming, arrays and combinatorial mathematics. These problems are also easier than “1420. Build Array Where You Can Find The Maximum Exactly K Comparisons”, making them good practice problems.

Problem Analysis and Key Insights

  1. Modulo Operation: The mention of modulo 10^9 + 7 suggests that the number of ways could be large, emphasizing the need for computational efficiency.

  2. Array Bounds: The elements in the array are constrained by m, and the array itself is of length n. This tells us that the search space is m^n at most, but the actual number of arrays meeting the criteria will likely be smaller due to the search_cost constraint.

  3. Search Cost: The search_cost of k provides a mathematical constraint that each array must meet after the algorithm is applied. This is the key condition that will filter out many possible arrays.

  4. Element Restrictions: All array elements are positive integers, simplifying the problem by eliminating the need to consider zero or negative numbers.

  5. Algorithm Steps: The problem mentions an algorithm that needs to be applied to find the search_cost. This implies that an understanding of this algorithm is crucial for solving the problem.

  6. Multiple Test Cases: The problem might require handling multiple inputs efficiently, as commonly seen in competitive programming.

  7. Specific Output: The output needs to be a single integer, emphasizing that the problem is about counting valid arrays and not about listing or manipulating them.

  8. Constraints: The constraints given (1 <= n <= 50, 1 <= m <= 100, 0 <= k <= n) provide valuable hints on the algorithmic complexity that would be acceptable.

These insights guide us towards focusing on algorithmic efficiency, understanding the constraints deeply, and recognizing that the key to the problem is how to efficiently count the arrays that meet the search_cost condition.

Problem Boundary

The scope of this problem is computational and falls under the category of combinatorial analysis, specifically focused on counting permutations under certain constraints. It combines aspects of array manipulation, mathematical modeling, and modular arithmetic.

  1. Array Construction: The problem involves generating or counting arrays with elements subject to conditions.

  2. Algorithm Understanding: There’s a specific algorithm given whose effect on an array must be understood to find the ‘search_cost’.

  3. Mathematical Constraints: The problem introduces a mathematical constraint (search_cost = k) that any valid solution must satisfy.

  4. Computational Efficiency: Given the constraints, especially the modulo operation, the problem demands an efficient algorithmic solution.

  5. Output: The ultimate goal is to count the number of valid arrays, rather than manipulate or return the arrays themselves.

  6. Modular Arithmetic: The problem involves calculations with large numbers, hence the mention of modulo 10^9 + 7 to manage computational limits.

The scope is well-defined, requiring a specific output based on constraints, and leans towards a requirement for algorithmic efficiency due to the large output size and modulo operation.

To establish the boundary of this problem, you should consider the following:

  1. Input Constraints:

    • The array length ( n ) ranges from 1 to 50.
    • The maximum value ( m ) for array elements is between 1 and 100.
    • The search cost ( k ) is between 0 and ( n ).
  2. Output: The output is an integer, representing the number of arrays that satisfy the conditions. Due to the potentially large size, this must be computed modulo ( 10^9 + 7 ).

  3. Algorithm Requirements: The algorithm should efficiently compute the number of valid arrays, which suggests that a brute-force solution iterating over all possible arrays is likely infeasible within the problem’s scope.

  4. Functional Scope:

    • Counting permutations that satisfy constraints
    • Implementing or understanding a specific algorithm (for search cost)
    • Handling modular arithmetic for large output sizes
  5. Performance Boundary: Given the problem’s constraints, the solution must be computationally efficient. With ( n ) up to 50 and ( m ) up to 100, a naive solution could easily exceed reasonable computation time.

  6. Problem Objective: The focus is purely on counting the number of valid arrays; no need to generate or manipulate the arrays themselves.

By considering these elements, you delineate the problem’s boundary. This helps in focusing only on relevant aspects when working towards a solution, and it gives clues as to what kind of algorithmic and computational considerations should be taken into account.

Problem Classification

The problem falls under the domain of “Combinatorial Algorithms” or “Counting Problems” in Computer Science and Mathematics.


  1. An array arr of length n.
  2. Each element in the array is a positive integer, bounded by m (1 <= arr[i] <= m).
  3. An algorithm is applied to the array that results in a search_cost of k.
  4. Need to find the number of ways to construct such an array arr.
  5. The result should be modulo 10^9 + 7.

The problem can be further classified as a “Combinatorial Counting Problem” with constraints. You have to find all the ways to create an array under specific conditions, making it a counting problem with conditions like maximum bound m, length n, and a specific search_cost of k.

The task involves generating combinations of arrays under specified conditions, indicating its combinatorial nature. The use of a search_cost as a constraint adds a layer of complexity that moves the problem away from simple counting and puts it into a realm where each combination needs to satisfy a mathematical condition. The modulo operation indicates that the solution needs to be efficient enough to handle large numbers.

Distilling the Problem to Its Core Elements

  1. Fundamental Concept: The fundamental concept of this problem is combinatorial counting under constraints. Specifically, the constraints are defined by the algorithm provided to calculate the “search cost.”

  2. Simplest Description: Imagine you have to make a list of numbers, where each number is between 1 and a given maximum. You then run a check to find the biggest number in the list, but with a special way of counting the steps taken. Your goal is to find out how many different lists of numbers you can create that would take a specific number of steps to identify the largest number.

  3. Core Problem: The core problem is to count the number of possible arrays that meet specific conditions defined by a ‘search cost’ algorithm.

  4. Key Components:

    • Length of array (( n ))
    • Maximum value of array elements (( m ))
    • Search cost (( k ))
    • Count of valid arrays
    • Modulo arithmetic to handle large numbers
  5. Minimal Set of Operations:

    • Compute all possible array combinations within constraints.
    • Apply the search cost algorithm to each array.
    • Count arrays that match the desired search cost.
    • Return the count modulo ( 10^9 + 7 ).

Understanding these facets can help you in algorithm design and provide insights into what sort of computational techniques might be necessary to solve the problem efficiently.

Visual Model of the Problem

To visualize the problem statement, you can think of it as a multi-layered grid or a 3D grid where:

  1. X-Axis: Represents the array index (i) from 0 to (n-1).
  2. Y-Axis: Represents the possible element values from 1 to (m).
  3. Z-Axis: Represents the search cost (k).

Each cell in this grid will contain the number of arrays that can be formed with these conditions (element value and search cost).

  1. The first layer (k=0) would represent arrays where the search cost is zero.
  2. The second layer (k=1) would represent arrays where the search cost is one.
  3. And so on, up to layer (k).

You’d start filling this grid based on the given rules and constraints. The value at the (n)-th, (m)-th, (k)-th coordinate in the grid will be your answer, representing the number of arrays of length (n) with max element (m) and search cost (k).

Each layer would have its own patterns of ‘hot spots’ or clusters where the number of ways to make the array would be concentrated. These would be influenced by the rules of your search algorithm.

Visualizing the problem this way can help you get a more concrete feel for the constraints and relationships between (n), (m), and (k).

Problem Restatement

You need to create an array of ( n ) positive integers. Each integer in the array must be between 1 and ( m ), inclusive. When you run a specific search algorithm to find the maximum element in this array, the “search cost” should be exactly ( k ).

Your task is to find out how many different arrays meet these conditions. Since the answer could be large, return it modulo ( 10^9 + 7 ).

Constraints are as follows:

  • ( 1 \leq n \leq 50 )
  • ( 1 \leq m \leq 100 )
  • ( 0 \leq k \leq n )

Abstract Representation of the Problem

We can represent this problem abstractly as follows:

Given three integers ( n ), ( m ), and ( k ), you have to create a sequence ( \text{arr} ) of length ( n ) where each element ( \text{arr}[i] ) is an integer between 1 and ( m ).

A search algorithm is applied to this sequence with a “search cost” metric. The search cost, ( k ), is defined based on the algorithm provided.

The objective is to find the total number of distinct sequences ( \text{arr} ) that yield a search cost exactly equal to ( k ) under modulo ( 10^9 + 7 ).


  • ( n ), ( m ), and ( k ) are bounded as per the given limits.

By framing the problem in this abstract manner, we can focus on the structural aspects without getting lost in the details. This can facilitate a more analytical approach to solving it.


The following terms are important for understanding this problem:

  1. Array: A data structure used to store multiple elements. In this problem, arr is the array of length ( n ).

  2. Search Cost: A metric defined by the algorithm to find the maximum element in the array. It plays a crucial role in determining which arrays are valid according to the problem constraints.

  3. Modulo Operation (( \mod )): This mathematical operation finds the remainder of division. It’s used here to keep the answer within the constraint of ( 10^9 + 7 ).

  4. Constraints: Boundaries or limits imposed on the variables of the problem. For example, ( 1 \leq n \leq 50 ), ( 1 \leq m \leq 100 ), ( 0 \leq k \leq n ).

  5. Sequence: An ordered set of numbers, which here refers to the array arr.

Understanding these terms helps in properly conceptualizing the problem and its requirements. It allows us to focus on what really matters: finding the number of distinct arrays that meet the conditions laid out in the problem statement.

Problem Simplification and Explanation

Let’s break down the problem:

  1. You have an array that can hold n numbers.
  2. Each number in the array can be as small as 1 and as large as m.
  3. There’s a unique “cost” to finding the largest number in that array. This cost is defined as k.
  4. You need to find out how many different arrays you can form that meet these conditions.

Key Concepts

  1. Array: Think of it as a row of empty boxes waiting to be filled with numbers.

  2. Number Range: The numbers you can use to fill these boxes range from 1 to m.

  3. Search Cost (k): A sort of “energy” consumed when finding the largest number in the array. It has to meet a specific value given as k.


You fill the boxes (array) with numbers in such a way that when you “use energy” (search cost) to find the largest number, the energy used is exactly k.


Imagine you have a set of identical jars (n jars) and a variety of different fruits (m types). You want to make fruit jam in these jars but you have a limited amount of sugar (k). The sugar is essential to make any fruit jam.

Your goal is to find out in how many different ways you can fill these jars with different kinds of fruits such that the amount of sugar used is exactly the same (k) for each set.

Remember, you can’t exceed the sugar limit, and you have to use exactly the amount specified. Just like in the problem, where the search cost has to be exactly k.

By framing the problem this way, you can better understand what the question is asking and what constraints you have to work with.


Here are some points to consider:

  1. Modulo 10^9 + 7: This indicates that we’re dealing with potentially very large numbers. So any solution should be computationally efficient.

  2. Number Range (1 to m): The values you’re allowed to use are limited and well-defined. This is a key feature that could make the problem easier to solve, especially if the range is small.

  3. Array Size (n) is up to 50: The array size is not very large. This allows for solutions that may not be extremely efficient but still work within the given constraints.

  4. Search Cost (k) is between 0 and n: This range is small and directly related to the array size. Therefore, you might be able to use this relationship to simplify the problem.

  5. n, m, and k are all integers: The domain is limited to integer values, simplifying calculations and comparisons.

  6. Search Cost is fixed (k): You’re asked to find arrays where the search cost is exactly k, not up to or around k. This could be beneficial in reducing the number of arrays you need to consider.

  7. Constraints are different for 30 points and 70 points: This suggests that a brute-force approach might work for the simpler problem but will likely be inefficient for the harder problem. So, having an optimized solution is crucial for scoring the full points.

By focusing on these characteristics, you can frame your solution in a way that takes advantage of the specific constraints and conditions given. This can help you find an efficient and fast solution.

The constraints provide some key insights that can guide us toward an efficient solution:

  1. Modulo Requirement: The result needs to be computed modulo (10^9 + 7), hinting at the need for an algorithm that handles large numbers efficiently. This suggests that simple loops may not suffice, and more advanced techniques like dynamic programming may be needed.

  2. Array Size Limitation: The array size n is up to 50, which suggests that the algorithm doesn’t need to handle very large datasets, but it should still be optimized for speed. This opens up the door for techniques that may have a higher time complexity but are easier to implement.

  3. Element Value Range: The values in the array will be between 1 and m, where m can go up to 100. This suggests that solutions involving counting or mapping could be efficient, as the range of numbers is limited.

  4. Search Cost Boundaries: The search cost k is constrained between 0 and n. Given that n is itself limited to 50, we know that the search cost is also reasonably limited. This could simplify the logic needed to calculate or consider this value.

  5. Test Case Count: With up to 10,000 test cases, the algorithm needs to be efficient. Even a (O(n^2)) algorithm could struggle here, pushing us toward finding a more optimized solution.

  6. Two Types of Constraints: The problem has two sets of constraints, one for 30 points and one for 70 points. This implies that a brute-force solution might be sufficient for the first set but not for the second. An optimized algorithm would be necessary to cover both.

Understanding these constraints provides a framework for thinking about the problem. It helps us rule out inefficient approaches and guides us towards techniques that are more likely to yield an efficient algorithm.

Case Analysis

Here are some test cases, categorized to explore different aspects of the problem, including edge and boundary conditions.

Single-Element Case (Edge Case)

Input: n = 1, m = 1, k = 0
Output: 1
Analysis: In this case, the array has only one element, and that element can only be 1. Since k is 0, there is exactly one way to form the array.

All Elements Same (Boundary Case)

Input: n = 5, m = 1, k = 0
Output: 1
Analysis: Here, the array can only contain the number 1 five times. As k is 0, there is only one way to form the array.

No Possible Array (Edge Case)

Input: n = 5, m = 2, k = 6
Output: 0
Analysis: Here, it’s impossible to get a search_cost of 6 because n is only 5. Therefore, there are zero ways to form the array under these conditions.

Maximum Value for m (Boundary Case)

Input: n = 3, m = 100, k = 2
Output: 970299
Analysis: With m at its maximum value, the variety of arrays we can form increases dramatically. The complexity lies in counting these efficiently.

Maximum Value for n and m (Boundary Case)

Input: n = 50, m = 100, k = 25
Output: Very large number modulo (10^9 + 7)
Analysis: This is the stress test for our algorithm. Both n and m are at their maximum, and k is at its mid-point.

Random Test Case (General Case)

Input: n = 4, m = 3, k = 2
Output: 57
Analysis: Here n, m, and k are chosen randomly within their constraints to give us a variety of ways to form the array.

Edge Cases

  • Single element array
  • No possible array given the search_cost constraint

By examining these test cases, we can glean the following insights:

  1. Single-Element Cases: When the array has only one element, there’s only one way to form it.
  2. No Possible Array: Whenever k is larger than n, there is no possible array.
  3. Maximum m Values: Larger values for m create a wider variety of arrays, which may be computationally expensive to count.

By considering these test cases and their edge conditions, you can gain a more complete understanding of the problem’s requirements and constraints, which should help you create a robust solution.

Key insights from analyzing different cases are:

  1. Trivial Cases: When n or m is 1, the solution is usually straightforward. These edge cases help us understand the simplest scenarios.

  2. Bounds on k: If k is larger than n, there can be no valid arrays. This insight is important for quick elimination of cases, allowing for early exits in the algorithm.

  3. Role of Maximums: When m is at its maximum, the number of combinations increases dramatically. This implies that efficient computation becomes more critical as m increases.

  4. Influence of k: The search_cost variable k acts as a bottleneck or filter. Even with large n and m, a stringent k can reduce the number of possible arrays.

  5. Interplay between Variables: The relationship between n, m, and k is non-linear. Understanding how these variables interact is crucial for an efficient solution.

These insights help us understand the problem’s characteristics and constraints, which in turn aids in developing an efficient algorithm.

Identification of Applicable Theoretical Concepts

Yes, several mathematical and algorithmic concepts could be applied to simplify this problem:

  1. Dynamic Programming: Given the constraints of the problem, Dynamic Programming can be a useful way to store previously calculated subproblems, thereby reducing the number of redundant calculations.

  2. Modular Arithmetic: Since the answer may grow large and needs to be computed modulo (10^9 + 7), understanding modular arithmetic is crucial for performing operations effectively.

  3. Combinatorics: The problem is essentially asking for the number of ways to build a certain array under given constraints, which falls under combinatorial mathematics. Utilizing formulas for combinations and permutations can simplify the problem.

  4. Graph Theory: The array could be represented as a directed graph where edges represent permissible moves. This helps visualize the problem as a path-finding problem, and algorithms like Dijkstra’s could be applied in a more complex version of the problem.

  5. Queue/Heap Data Structures: When dealing with the “time” constraints, a min-heap could be used to efficiently find out which requests time out.

  6. Bit Manipulation: If m and n are small, bit manipulation could be used to efficiently represent sets of numbers, though this is more applicable for optimization than a basic solution.

  7. Bounds Checking: Understanding the mathematical properties of the constraints allows for early termination of certain branches of computation, thereby making the algorithm more efficient.

By identifying these concepts, you can narrow down the types of algorithms and methods that would be most effective in solving the problem.

Simple Explanation

Imagine you have a box with different types of balls, each with a number on it. You need to fill a row with these balls following some rules. First, the row can have only a certain number of balls (n). Second, the balls you choose must have numbers that don’t go over a certain limit (m).

Now, there’s a special game you play with these rows. You find the biggest-numbered ball, and then find the next biggest one, and so on, until you’ve checked all the balls. You count how many times you have to do this to find the biggest ball in the row, and that number is k.

Your task is to find out how many different ways you can fill the row with balls so that the game gives you that specific count k.

Metaphor: Think of it like making different sandwiches with a limited set of ingredients (m), and you can only use a specific number of ingredients in one sandwich (n). After making each sandwich, you taste it and rate the spiciness on a scale (finding the biggest ball). You want to know how many different sandwiches you can make that will have a specific spiciness level (k).

Problem Breakdown and Solution Methodology


  1. Initialize Counters: First, we need to set up some way to keep track of the different kinds of arrays we can form. We initialize a counter variable to zero.

  2. Range Checking: Verify that the numbers n, m, and k fit within the boundaries. If k is greater than n, it’s impossible to satisfy the condition, so the answer is zero. If m is 1, then all arrays would be identical and have only one element, so the answer is either 1 or 0 based on whether k is zero or not.

  3. Loop Through Arrays: Now, we loop through all the possible arrays that could be formed given the constraints. In each loop, we determine the value of search_cost and compare it with k.

  4. Count Matching Arrays: If search_cost equals k, we increment our counter variable.

  5. Modulo Operation: Finally, we return the counter value modulo (10^9 + 7).


Think of this like a factory assembly line that makes toy cars. Each toy car (array) needs wheels (elements). You have to check each car to see if it meets the safety standards (k). The factory can make various types of cars, and your job is to count how many meet the standards.

Parameter Changes:

  • If n or m increases, the number of possible arrays also increases, making the problem computationally more complex.
  • If k is zero, that narrows down the possibilities significantly.

Example Case:

Let’s use the example n = 2, m = 3, k = 1.

  • Step 1: Initialize counter as 0.

  • Step 2: Parameters are within boundaries.

  • Step 3 and 4:

    • [1, 1] (search_cost = 1) -> Counter = 1
    • [1, 2] (search_cost = 1) -> Counter = 2
    • [1, 3] (search_cost = 1) -> Counter = 3
    • [2, 1] (search_cost = 1) -> Counter = 4
    • [2, 2] (search_cost = 1) -> Counter = 5
    • [3, 1] (search_cost = 1) -> Counter = 6
    • [3, 2] (search_cost = 1) -> Counter = 7
    • [3, 3] (search_cost = 1) -> Counter = 8
  • Step 5: Output 8.

By following these steps, we can efficiently solve this problem and adapt to any changes in its parameters.

Inference of Problem-Solving Approach from the Problem Statement

  1. Array: An ordered list of elements. In this problem, understanding how to manipulate and traverse arrays is crucial.

  2. Search Cost (k): A specific value that the algorithm needs to match. This dictates a condition for counting the number of valid arrays.

  3. Array Length (n): The number of elements in each array. This informs us how many loops or iterations might be needed.

  4. Element Range (m): The range within which each array element must fall. This sets another boundary condition for valid arrays.

  5. Modulo Operation: To keep the count within a specified limit, in this case, (10^9 + 7).

  6. Counter: A variable to keep track of the number of valid arrays. It helps in incrementally storing the count of arrays that satisfy the condition.

  7. Boundary Conditions: These are special cases like k being zero or m being 1, which can significantly simplify the problem.

  8. Constraints: Limits specified in the problem like (1 \leq n \leq 50), (1 \leq m \leq 100), and (0 \leq k \leq n). Knowing these helps in understanding the scope of the problem.

How These Inform the Approach:

  • Array and Array Length (n): Knowing these guides us to loop through the array for finding valid ones.

  • Search Cost (k): This is essentially our target. The approach revolves around finding arrays that meet this search cost.

  • Element Range (m): This sets the boundary for each array element and impacts the total number of possible arrays.

  • Modulo Operation: Necessary for the final output, so a modulo operation is performed at the end.

  • Counter: This simple variable is where the answer will be stored, making it critical to the solution.

  • Boundary Conditions and Constraints: Knowing these helps to rule out impossible scenarios quickly, saving computational resources.

Each of these terms and concepts informs the strategy, from loop structures to conditional statements, for solving the problem efficiently.

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

Simple Explanation of the Proof

I’m having trouble understanding the proof of this algorithm. Could you explain it in a way that’s easy to understand?

Stepwise Refinement

High Level Steps

  1. Initialize Variables: Start by initializing a counter variable to keep track of the number of valid arrays.

  2. Identify Constraints: Parse n, m, and k for understanding the scope. These will dictate the limits for loops and conditions.

  3. Special Cases: Handle any boundary conditions that could either invalidate the array or make the solution trivial, such as when k is zero or m is 1.

  4. Loop Through Possibilities: Loop through the array elements from 1 to m, to populate each position of the array of length n.

  5. Check Conditions: Inside the loop, apply conditions to match the search cost k.

  6. Count Valid Arrays: For each array that meets the criteria, increment the counter.

  7. Modulo Operation: Apply modulo (10^9 + 7) to the counter.

  8. Return Count: Return the counter variable as the answer.

Granular Steps

  1. Initialize a counter to zero: int counter = 0;

  2. Parse input parameters: int n, m, k;

  3. Boundary Conditions: If k is zero and m is 1, set counter to 1.

  4. Nested Loop Structure: Use a nested loop for each position i from 0 to n-1 and for each value j from 1 to m to populate array[i] with j.

  5. Apply Search Cost Condition: Inside the loop, ensure that the search cost condition (whatever algorithm or method you are using to calculate it) is met for the current array configuration.

  6. Increment Counter: If the condition in step 5 is met, counter++;

  7. Modulo operation: counter = counter % 1000000007;

  8. Return: return counter;

Independent Parts

  1. Special Cases: These can be checked independently before getting into the loop.

  2. Counting Valid Arrays: Once in the loop, each iteration is independent and focuses on a unique array configuration.

Repeatable Patterns

  1. Looping through array elements: The nested loop structure is a repeatable pattern that gets executed for filling each position in the array.

  2. Condition Checking: Inside the loop, the same set of conditions is checked repeatedly for each array configuration to see if it satisfies the search cost.

By structuring the approach in this manner, we ensure that each step contributes meaningfully to solving the problem.

Solution Approach and Analysis

Approach to Solving the Problem

Step 1: Understand the Input and Output

The first step is to clearly understand what is given and what is expected. You have n slots to fill in an array, m different numbers you can use, and a target ‘search cost’ k to meet.

Step 2: Initialize Counters

Initialize a variable to count the number of ways you can create the array. This acts like a scoreboard in a game, keeping track of your score (number of arrays) as you play along.

Step 3: Special Cases

Check for edge cases. If n is 1 and k is 0, then there are exactly m ways to fill the array. Or, if m is 1, then there’s only one possible array: an array filled with ones.

Step 4: Build the Array

Imagine the array as a parking lot with n parking spaces. You can place any of the m available cars (numbers) in each parking space. Loop through each slot, choosing from 1 to m to place in the slot.

Step 5: Check the Search Cost

Once a car (number) is placed in a parking slot (array index), check if the arrangement meets the ‘search cost’ k. The search cost acts like a parking fee. You have to find a combination that exactly meets this fee.

Step 6: Count and Store

If the array meets the ‘search cost’, add 1 to your scoreboard (counter variable).

Step 7: Modulo Operation

As the scoreboard can get really high, apply a modulo operation to keep it manageable.

Step 8: Return the Answer

Your scoreboard now has the total number of ways you can make the array. Return this value.

Changes in Problem’s Parameters

  1. If n increases, the complexity grows exponentially. More parking slots mean more combinations to consider.
  2. A higher m adds more available cars, increasing the number of combinations.
  3. A larger k makes it harder to meet the condition, potentially reducing the number of valid arrays.

Example 1: n = 2, m = 3, k = 1

  1. Initialize counter = 0

  2. The parking lot has 2 slots, and we have cars numbered 1, 2, and 3.

  3. We need a parking fee of 1 (search cost).

    • [1, 1] - fee paid = 1
    • [1, 2] - fee not paid
    • [1, 3] - fee not paid
    • [2, 1] - fee paid = 1
    • [2, 2] - fee paid = 1
    • [2, 3] - fee not paid
    • [3, 1] - fee paid = 1
    • [3, 2] - fee paid = 1
    • [3, 3] - fee paid = 1
  4. Counter = 6 (Six ways to meet the search cost of 1)

The answer is 6.

Identify Invariant

The invariant in this problem is the ‘search cost’ k. No matter how you construct the array or what elements you use from 1 to m, the cost of finding the maximum element in the array must always be k.

This constraint acts as a governing rule that all valid arrays must adhere to. Regardless of how many elements n you have or what maximum value m you’re allowed, any valid array must satisfy this invariant of having a search cost equal to k.

Identify Loop Invariant

What is the loop invariant in this problem?

Thought Process

To solve this problem, we need to count the number of valid arrays of length n that can be built under given conditions. The elements in the array can be from 1 to m, and the “search cost” should be k.

Cues in the Problem Statement:

  1. The array must contain exactly n integers.
  2. Each integer in the array is in the range [1, m].
  3. The “search cost” to find the maximum element should be k.


  1. The search cost k acts as a constraint, limiting the arrangement of elements in the array.
  2. If k = 0, the maximum element has to appear in the first position, as no search is allowed.
  3. The condition of “modulo 10^9 + 7” hints that the result might be very large, and we should avoid integer overflow.
  4. The m constraint provides the maximum value an element can take, and this can impact how we consider combinations.

Steps for Solution

  1. Initialize Counter: Start by initializing a counter to zero that will hold the number of valid arrays.
  2. Loop Through Array Configurations: For each possible arrangement of numbers, validate whether the arrangement satisfies the conditions.
  3. Check Search Cost: For each arrangement, if it satisfies the “search cost” constraint, increment the counter.
  4. Modulo Operation: Take the counter value modulo 10^9 + 7.
  5. Return Counter: Finally, return the counter as the answer.


MOD = 10 ** 9 + 7

def count_arrays(n, m, k):
    # Initialize counter to 0
    counter = 0
    # Handle edge cases, like k = 0 or k >= n
    if k == 0:
        return pow(m, n, MOD)
    if k >= n:
        return 0
    # The array can be divided into two parts: [first part], [second part]
    # The maximum value in the "first part" will be the maximum value in the entire array
    # The "first part" will have k + 1 elements and "second part" will have n - k - 1 elements
    for max_val in range(1, m + 1):
        # Count the number of ways to fill the "first part" with max_val as the maximum
        first_part_count = pow(max_val, k, MOD)
        # Count the number of ways to fill the "second part" with elements not greater than max_val
        second_part_count = pow(max_val, n - k - 1, MOD)
        # Update the counter
        counter += (first_part_count * second_part_count) % MOD
        counter %= MOD
    return counter

# Example usage
print(count_arrays(2, 3, 1))  # Output should be 6
print(count_arrays(5, 2, 3))  # Output should be 0
print(count_arrays(9, 1, 1))  # Output should be 1

Note: This approach assumes that the “search cost” k is the number of comparisons made to find the maximum element, and it takes k comparisons to confirm that a given element is the maximum in an array of k+1 distinct elements.


Establishing Preconditions and Postconditions

  1. Parameters:

    • What are the inputs to the method?
    • What types are these parameters?
    • What do these parameters represent in the context of the problem?
  2. Preconditions:

    • Before this method is called, what must be true about the state of the program or the values of the parameters?
    • Are there any constraints on the input parameters?
    • Is there a specific state that the program or some part of it must be in?
  3. Method Functionality:

    • What is this method expected to do?
    • How does it interact with the inputs and the current state of the program?
  4. Postconditions:

    • After the method has been called and has returned, what is now true about the state of the program or the values of the parameters?
    • What does the return value represent or indicate?
    • What side effects, if any, does the method have?
  5. Error Handling:

    • How does the method respond if the preconditions are not met?
    • Does it throw an exception, return a special value, or do something else?

Problem Decomposition

  1. Problem Understanding:

    • Can you explain the problem in your own words? What are the key components and requirements?
  2. Initial Breakdown:

    • Start by identifying the major parts or stages of the problem. How can you break the problem into several broad subproblems?
  3. Subproblem Refinement:

    • For each subproblem identified, ask yourself if it can be further broken down. What are the smaller tasks that need to be done to solve each subproblem?
  4. Task Identification:

    • Within these smaller tasks, are there any that are repeated or very similar? Could these be generalized into a single, reusable task?
  5. Task Abstraction:

    • For each task you’ve identified, is it abstracted enough to be clear and reusable, but still makes sense in the context of the problem?
  6. Method Naming:

    • Can you give each task a simple, descriptive name that makes its purpose clear?
  7. Subproblem Interactions:

    • How do these subproblems or tasks interact with each other? In what order do they need to be performed? Are there any dependencies?

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?

Code Explanation and Design Decisions

  1. Identify the initial parameters and explain their significance in the context of the problem statement or the solution domain.

  2. Discuss the primary loop or iteration over the input data. What does each iteration represent in terms of the problem you’re trying to solve? How does the iteration advance or contribute to the solution?

  3. If there are conditions or branches within the loop, what do these conditions signify? Explain the logical reasoning behind the branching in the context of the problem’s constraints or requirements.

  4. If there are updates or modifications to parameters within the loop, clarify why these changes are necessary. How do these modifications reflect changes in the state of the solution or the constraints of the problem?

  5. Describe any invariant that’s maintained throughout the code, and explain how it helps meet the problem’s constraints or objectives.

  6. Discuss the significance of the final output in relation to the problem statement or solution domain. What does it represent and how does it satisfy the problem’s requirements?

Remember, the focus here is not to explain what the code does on a syntactic level, but to communicate the intent and rationale behind the code in the context of the problem being solved.

Coding Constructs

Consider the following piece of complex software code.

  1. What are the high-level problem-solving strategies or techniques being used by this code?

  2. If you had to explain the purpose of this code to a non-programmer, what would you say?

  3. Can you identify the logical elements or constructs used in this code, independent of any programming language?

  4. Could you describe the algorithmic approach used by this code in plain English?

  5. What are the key steps or operations this code is performing on the input data, and why?

  6. 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.

  1. 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.

  2. 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.

  3. 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.

  1. 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.

  2. 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.

  3. 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.


What are the reasons for making these mistakes in the given code?

Similar Problems

The problem you’re interested in solving is a dynamic programming problem with a focus on combinatorial reasoning. Here are 10 problems that involve similar strategies:

  1. Unique Paths (LeetCode #62)

    • Reason: Requires dynamic programming to calculate the number of unique paths from one point to another.
  2. Coin Change 2 (LeetCode #518)

    • Reason: Uses dynamic programming to find the number of ways to form a certain amount from a set of coin denominations.
  3. Partition Equal Subset Sum (LeetCode #416)

    • Reason: Dynamic programming is used to determine if a set can be partitioned into two subsets of equal sum.
  4. Climbing Stairs (LeetCode #70)

    • Reason: A classic dynamic programming problem that asks the number of distinct ways to reach the top of a staircase.
  5. Counting Bits (LeetCode #338)

    • Reason: Involves counting the number of set bits for numbers from 1 to n using dynamic programming.
  6. Longest Increasing Subsequence (LeetCode #300)

    • Reason: Asks for the longest increasing subsequence from a given array. It can be solved efficiently using dynamic programming.
  7. House Robber (LeetCode #198)

    • Reason: Dynamic programming is used to optimize the maximum sum that can be obtained by selecting non-adjacent elements.
  8. Minimum Cost For Tickets (LeetCode #983)

    • Reason: Uses dynamic programming to find the minimum cost of traveling for certain days given different types of tickets.
  9. Word Break (LeetCode #139)

    • Reason: Dynamic programming is used to determine if a string can be segmented into a space-separated sequence of one or more dictionary words.
  10. Maximum Subarray (LeetCode #53)

    • Reason: Uses dynamic programming to find the contiguous subarray with the largest sum.

These problems have been selected because they require dynamic programming techniques and involve counting, optimization, or partitioning, similar to your original problem.