Solution Approach

The implementation uses a higher-order function pattern - a function that returns another function. Let's break down the solution step by step:

Function Signature:

function compose(functions: F[]): F

• Takes an array of functions as input
• Returns a new function of the same type F (a function that accepts and returns a number)

Creating the Composed Function:

return function (x) {  return functions.reduceRight((acc, fn) => fn(acc), x); };

The solution creates and returns an anonymous function that:

1. Accepts a single parameter x (the initial input)
2. Uses reduceRight to process the functions array

How reduceRight Works Here:

• Callback function: (acc, fn) => fn(acc)
  • acc is the accumulator (the running result)
  • fn is the current function being processed
  • Simply applies the current function to the accumulated value
• Initial value: x (the input to our composed function)
• Direction: Processes array from right to left

Execution Flow Example: For compose([f, g, h])(x):

1. Start with accumulator = x
2. Process h: accumulator = h(x)
3. Process g: accumulator = g(h(x))
4. Process f: accumulator = f(g(h(x)))
5. Return final accumulator value

Edge Case Handling:

• Empty array: When functions is empty, reduceRight returns the initial value x unchanged, automatically giving us the identity function behavior
• Single function: Works correctly, applying just that one function
• Multiple functions: Chains them in the correct right-to-left order

The beauty of this solution lies in its simplicity - reduceRight naturally handles the composition pattern without any explicit conditionals or loops, making the code both readable and efficient.

Example Walkthrough

Let's trace through a concrete example to understand how the solution works.

Given:

functions = [x => x + 1, x => x * x, x => 2 * x] compose(functions)(5)

Step-by-step execution:

1. Initial Setup:

   • We call the composed function with x = 5
   • reduceRight starts from the rightmost function
   • Initial accumulator = 5

2. First iteration (rightmost function: x => 2 * x):

   • Current function: fn = x => 2 * x
   • Current accumulator: acc = 5
   • Apply function: fn(acc) = 2 * 5 = 10
   • New accumulator = 10

3. Second iteration (middle function: x => x * x):

   • Current function: fn = x => x * x
   • Current accumulator: acc = 10
   • Apply function: fn(acc) = 10 * 10 = 100
   • New accumulator = 100

4. Third iteration (leftmost function: x => x + 1):

   • Current function: fn = x => x + 1
   • Current accumulator: acc = 100
   • Apply function: fn(acc) = 100 + 1 = 101
   • New accumulator = 101

5. Final Result:

   • reduceRight completes and returns 101
   • The composed function returns 101

Verification: This is equivalent to computing (x + 1)((x * x)(2 * x)) with x = 5:

• First: 2 * 5 = 10
• Then: 10 * 10 = 100
• Finally: 100 + 1 = 101 ✓

Empty Array Case:

functions = [] compose(functions)(5)

• reduceRight with empty array returns initial value unchanged
• Result: 5 (identity function behavior)

Time and Space Complexity

Time Complexity: O(n) where n is the number of functions in the array.

When compose is called, it returns a new function immediately in O(1) time. However, when the returned function is invoked with a value x, it executes reduceRight which iterates through all n functions in the array, applying each function exactly once. Each function application is assumed to be O(1), so the total time complexity of executing the composed function is O(n).

Space Complexity: O(n) where n is the number of functions in the array.

The space complexity includes:

• The returned function holds a reference to the functions array through closure, which takes O(n) space
• During execution of reduceRight, the call stack depth is O(1) since reduceRight is implemented iteratively in JavaScript
• The intermediate value acc takes O(1) space

Therefore, the overall space complexity is O(n) due to storing the reference to the functions array in the closure.

Common Pitfalls

1. Incorrect Order of Composition

The most common mistake is applying functions in left-to-right order instead of right-to-left. Many developers intuitively think of reading the array from start to end.

Wrong Implementation:

def compose(functions: List[F]) -> F:  def composed_function(x: float) -> float:  accumulator = x  for fn in functions: # Wrong: iterates left to right  accumulator = fn(accumulator)  return accumulator  return composed_function

Impact: For compose([f, g, h])(x), this gives f(g(h(x))) instead of the correct f(g(h(x))). Wait, that looks the same! But consider compose([x => x + 1, x => 2 * x])(4):

• Wrong: (4 + 1) * 2 = 10
• Correct: (2 * 4) + 1 = 9

Solution: Always use reversed() or iterate backwards through the array.

2. Mutating the Original Functions Array

Some implementations might accidentally modify the input array when trying to reverse it.

Wrong Implementation:

def compose(functions: List[F]) -> F:  functions.reverse() # Mutates the original array!  def composed_function(x: float) -> float:  accumulator = x  for fn in functions:  accumulator = fn(accumulator)  return accumulator  return composed_function

Solution: Use reversed() which returns an iterator without modifying the original list, or create a copy before reversing.

3. Type Confusion with Return Values

Forgetting that each function must return a value that can be passed to the next function.

Wrong Usage:

# This will fail if any function returns None functions = [  lambda x: print(x), # Returns None!  lambda x: x * 2 ]

Solution: Ensure all functions in the array return appropriate values:

functions = [  lambda x: (print(f"Value: {x}"), x)[1], # Prints and returns x  lambda x: x * 2 ]

4. Not Handling Edge Cases Properly

Explicitly checking for empty arrays with unnecessary conditional logic.

Overcomplicated Implementation:

def compose(functions: List[F]) -> F:  def composed_function(x: float) -> float:  if len(functions) == 0: # Unnecessary check  return x  accumulator = x  for fn in reversed(functions):  accumulator = fn(accumulator)  return accumulator  return composed_function

Solution: The loop naturally handles empty arrays - when there are no functions to iterate over, the accumulator remains as the initial value x, giving us the identity function behavior automatically.

5. Performance Issues with Large Function Arrays

Creating unnecessary intermediate data structures or copies.

Inefficient Implementation:

def compose(functions: List[F]) -> F:  def composed_function(x: float) -> float:  reversed_list = list(reversed(functions)) # Creates unnecessary copy  accumulator = x  for fn in reversed_list:  accumulator = fn(accumulator)  return accumulator  return composed_function

Solution: Use reversed() directly as an iterator without converting to a list, or use negative indexing to iterate backwards without creating any copies:

def compose(functions: List[F]) -> F:  def composed_function(x: float) -> float:  accumulator = x  for i in range(len(functions) - 1, -1, -1):  accumulator = functions[i](accumulator)  return accumulator  return composed_function