Memoization Top Down DP in DSA Go - Time & Space Complexity

Time Complexity: Memoization Top Down DP

O(n)

Understanding Time Complexity

We analyze how memoization in top-down dynamic programming speeds up repeated calculations.

We want to know how the number of operations changes as input size grows.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

package main

import "fmt"

func fibMemo(n int, memo map[int]int) int {
    if n <= 1 {
        return n
    }
    if val, ok := memo[n]; ok {
        return val
    }
    memo[n] = fibMemo(n-1, memo) + fibMemo(n-2, memo)
    return memo[n]
}

func main() {
    memo := make(map[int]int)
    fmt.Println(fibMemo(10, memo))
}

This code calculates the nth Fibonacci number using memoization to avoid repeated work.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls to fibMemo with memo checks.
• How many times: Each unique input n is computed once; repeated calls return stored results immediately.

How Execution Grows With Input

Each number from 0 up to n is computed once, so operations grow roughly in a straight line with n.

Input Size (n)	Approx. Operations
10	About 10 computations
100	About 100 computations
1000	About 1000 computations

Pattern observation: The work grows linearly as input size increases.

Final Time Complexity

Time Complexity: O(n)

This means the time taken grows in direct proportion to the input size n.

Common Mistake

[X] Wrong: "Memoization still does all recursive calls, so it's exponential time like plain recursion."

[OK] Correct: Memoization stores results so each input is computed once, avoiding repeated work and reducing time drastically.

Interview Connect

Understanding memoization's time savings shows you can optimize recursive solutions, a key skill in many coding challenges.

Self-Check

"What if we removed memoization and used plain recursion? How would the time complexity change?"

Practice

1. What is the main purpose of memoization in top-down dynamic programming?

easy

A. To increase the size of the input for better accuracy

B. To sort the input data before processing

C. To convert recursive code into iterative code

D. To store results of subproblems to avoid repeated calculations

Solution

1. Step 1: Understand memoization concept

   Memoization saves the results of subproblems so they are not recalculated multiple times.

2. Step 2: Compare options

   Only To store results of subproblems to avoid repeated calculations correctly describes this purpose; others describe unrelated actions.

3. Final Answer:

   To store results of subproblems to avoid repeated calculations -> Option D

4. Quick Check:

   Memoization = Store subproblem results [OK]

Hint: Memoization means caching answers to reuse later [OK]

Common Mistakes:

• Confusing memoization with sorting
• Thinking memoization changes recursion to iteration
• Believing memoization increases input size

2. Which of the following Go code snippets correctly initializes a memoization map for storing integer results?

easy

A. memo := []int{}

B. memo := map[string]string{}

C. memo := make(map[int]int)

D. memo := new(int)

Solution

1. Step 1: Identify correct map initialization

   In Go, a map from int to int is created with make(map[int]int).

2. Step 2: Check other options

   memo := []int{} is a slice, not a map; C uses wrong types; D creates a pointer to int, not a map.

3. Final Answer:

   memo := make(map[int]int) -> Option C

4. Quick Check:

   Map[int]int = make(map[int]int) [OK]

Hint: Use make(map[keyType]valueType) to create memo map [OK]

Common Mistakes:

• Using slice instead of map for memo
• Wrong key or value types in map
• Using new(int) which is a pointer, not a map

3. What will be the output of the following Go code using memoization for Fibonacci numbers?

package main
import "fmt"

var memo = make(map[int]int)

func fib(n int) int {
    if n <= 1 {
        return n
    }
    if val, ok := memo[n]; ok {
        return val
    }
    memo[n] = fib(n-1) + fib(n-2)
    return memo[n]
}

func main() {
    fmt.Println(fib(5))
}

medium

A. 5

B. 8

C. 13

D. Error: stack overflow

Solution

1. Step 1: Trace fib(5) calls with memoization

   fib(5) = fib(4) + fib(3), fib(4) = fib(3) + fib(2), fib(3) = fib(2) + fib(1), etc. Memo stores results to avoid repeats.

2. Step 2: Calculate fib(5)

   fib(0)=0, fib(1)=1, fib(2)=1, fib(3)=2, fib(4)=3, fib(5)=5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   fib(5) = 5 [OK]

Hint: Memoization avoids repeated fib calls, output fib(5)=5 [OK]

Common Mistakes:

• Confusing fib(5) with fib(6)
• Ignoring memo and recalculating
• Expecting runtime error due to recursion

4. Identify the bug in this Go memoized Fibonacci function and select the fix:

func fib(n int, memo map[int]int) int {
    if n <= 1 {
        return n
    }
    if val, ok := memo[n]; ok {
        return val
    }
    memo[n] = fib(n-1, memo) + fib(n-2, memo)
    return memo[n]
}

func main() {
    fmt.Println(fib(5, nil))
}

medium

A. Remove memo parameter from fib

B. Initialize memo map before passing to fib

C. Change base case to n < 0

D. Return 0 when n <= 1

Solution

1. Step 1: Analyze memo usage in fib call

   fib is called with memo = nil, so accessing memo[n] causes runtime panic.

2. Step 2: Fix by initializing memo map before first call

   Pass a properly initialized map, e.g., make(map[int]int), to avoid nil map access.

3. Final Answer:

   Initialize memo map before passing to fib -> Option B

4. Quick Check:

   Nil map causes panic; initialize before use [OK]

Hint: Always initialize map before passing to recursive function [OK]

Common Mistakes:

• Passing nil map causing runtime panic
• Changing base case incorrectly
• Removing memo parameter loses memoization

5. Given the following Go code for memoized calculation of the number of ways to climb stairs (1 or 2 steps at a time), what is the output of countWays(4, memo)?

package main
import "fmt"

func countWays(n int, memo map[int]int) int {
    if n == 0 {
        return 1
    }
    if n < 0 {
        return 0
    }
    if val, ok := memo[n]; ok {
        return val
    }
    memo[n] = countWays(n-1, memo) + countWays(n-2, memo)
    return memo[n]
}

func main() {
    memo := make(map[int]int)
    fmt.Println(countWays(4, memo))
}

hard

A. 5

B. 7

C. 8

D. Error: infinite recursion

Solution

1. Step 1: Understand problem and base cases

   countWays(0) = 1 (one way to stand still), countWays(n<0) = 0 (no way). Ways to climb n steps = sum of ways for n-1 and n-2.

2. Step 2: Calculate countWays(4)

   countWays(1)=1, countWays(2)=2, countWays(3)=3, countWays(4)=5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   Ways to climb 4 steps = 5 [OK]

Hint: Count ways = fib(n+1), so countWays(4) = 5 [OK]

Common Mistakes:

• Confusing base cases causing wrong counts
• Forgetting to initialize memo map
• Expecting infinite recursion without memo