Greedy vs DP How to Know Which to Apply in DSA Go - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if a problem can be solved greedily by comparing choices.

DSA Go

func canUseGreedy(choices []int) bool {
    for i := 0; i < len(choices)-1; i++ {
        if choices[i] [1] choices[i+1] {
            return false
        }
    }
    return true
}

Drag options to blanks, or click blank then click option'

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' causes wrong order check.

We check if each choice is less than the next to ensure greedy choice property.

2fill in blank

medium

Complete the code to decide if dynamic programming is needed by checking overlapping subproblems.

DSA Go

func hasOverlappingSubproblems(subproblems map[string]bool, key string) bool {
    if _, exists := subproblems[1] key; exists {
        return true
    }
    return false
}

Drag options to blanks, or click blank then click option'

A[

B(

C<-

D{

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses or braces instead of square brackets for map access.

In Go, map keys are accessed using square brackets [].

3fill in blank

hard

Fix the error in the code that tries to memoize results in dynamic programming.

DSA Go

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

Drag options to blanks, or click blank then click option'

A[

B(

C{

D<-

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses causes syntax error.

Accessing map elements in Go requires square brackets [].

4fill in blank

hard

Fill both blanks to create a function that decides between greedy and DP based on problem properties.

DSA Go

func chooseApproach(hasGreedyProperty bool, hasOverlappingSubproblems bool) string {
    if hasGreedyProperty [1] hasOverlappingSubproblems {
        return "Use Greedy"
    } else if !hasGreedyProperty && hasOverlappingSubproblems [2] {
        return "Use DP"
    }
    return "Analyze further"
}

Drag options to blanks, or click blank then click option'

A&&

B||

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using OR (||) causes wrong logic.

We use logical AND (&&) to check both conditions must be true.

5fill in blank

hard

Fill all three blanks to implement a function that returns the approach name based on problem features.

DSA Go

func approachName(greedy bool, overlapping bool, optimalSubstructure bool) string {
    if greedy [1] overlapping [2] optimalSubstructure {
        return "Greedy"
    } else if !greedy [3] overlapping && optimalSubstructure {
        return "Dynamic Programming"
    }
    return "Unknown"
}

Drag options to blanks, or click blank then click option'

A&&

B||

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using OR (||) leads to incorrect approach selection.

Logical AND (&&) is used to ensure all conditions are true for the approach.

Practice

1. Which of the following best describes when to use a greedy algorithm instead of dynamic programming?

easy

A. When the problem requires exploring all possible solutions

B. When the problem is unsolvable by any algorithm

C. When the problem has overlapping subproblems but no optimal substructure

D. When the problem has the greedy choice property and optimal substructure

Solution

1. Step 1: Understand greedy choice property

   A greedy algorithm works when choosing the best option at each step leads to a global optimum.

2. Step 2: Check optimal substructure

   Optimal substructure means the optimal solution can be built from optimal solutions of subproblems.

3. Final Answer:

   When the problem has the greedy choice property and optimal substructure -> Option D

4. Quick Check:

   Greedy = greedy choice + optimal substructure [OK]

Hint: Use greedy if local best leads to global best [OK]

Common Mistakes:

• Confusing overlapping subproblems with greedy applicability
• Using greedy when problem needs full exploration
• Ignoring optimal substructure requirement

2. Which Go code snippet correctly declares a memoization map for dynamic programming?

easy

A. memo := new(map[int]int)

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

C. memo := map[]int{}

D. memo := []int{}

Solution

1. Step 1: Understand Go map declaration

   In Go, maps are declared with make(map[keyType]valueType) to initialize properly.

2. Step 2: Check each option

   memo := make(map[int]int) uses make with correct syntax. memo := []int{} is a slice, not a map. memo := map[]int{} has invalid syntax. memo := new(map[int]int) uses new which returns pointer, not initialized map.

3. Final Answer:

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

4. Quick Check:

   Use make(map[key]value) for maps [OK]

Hint: Use make(map[key]value) to create maps in Go [OK]

Common Mistakes:

• Using new(map[int]int) which returns nil map pointer
• Confusing slices with maps
• Incorrect map syntax like map[]int{}

3. What is the output of the following Go code snippet?

func max(a, b int) int {
    if a > b {
        return a
    }
    return b
}

func main() {
    weights := []int{1, 3, 4, 5}
    values := []int{1, 4, 5, 7}
    capacity := 7
    dp := make([]int, capacity+1)

    for i := 0; i < len(weights); i++ {
        for w := capacity; w >= weights[i]; w-- {
            dp[w] = max(dp[w], dp[w-weights[i]] + values[i])
        }
    }
    fmt.Println(dp[capacity])
}

medium

A. 12

B. 10

C. 11

D. 9

Solution

1. Step 1: Understand the code - 0/1 Knapsack DP

   The code uses bottom-up DP to find max value for capacity 7 with given weights and values.

2. Step 2: Trace DP array updates

   Items: (1,1), (3,4), (4,5), (5,7). DP updates from capacity down to weight. Final dp[7] = max value achievable is 11.

3. Final Answer:

   11 -> Option C

4. Quick Check:

   DP knapsack max value = 11 [OK]

Hint: Trace DP from largest capacity down to weight [OK]

Common Mistakes:

• Iterating w from low to high causing wrong results
• Confusing weights and values arrays
• Forgetting to update dp[w] with max

4. Identify the bug in this greedy approach for coin change problem in Go:

func coinChange(coins []int, amount int) int {
    count := 0
    for _, coin := range coins {
        count += amount / coin
        amount %= coin
    }
    if amount != 0 {
        return -1
    }
    return count
}

func main() {
    coins := []int{1, 3, 4}
    amount := 6
    fmt.Println(coinChange(coins, amount))
}

medium

A. Coins slice is not sorted descending

B. Using integer division causes wrong count

C. No base case for amount zero

D. Returns -1 even if solution exists

Solution

1. Step 1: Analyze greedy coin change logic

   The greedy approach works only if coins are sorted descending to pick largest coin first.

2. Step 2: Check coins order

   Coins are {1,3,4} ascending, so greedy picks 1 first, leading to wrong minimal count.

3. Final Answer:

   Coins slice is not sorted descending -> Option A

4. Quick Check:

   Sort coins descending for greedy coin change [OK]

Hint: Sort coins descending before greedy coin change [OK]

Common Mistakes:

• Not sorting coins descending
• Assuming greedy always works for coin change
• Ignoring leftover amount check

5. You want to find the minimum number of coins to make amount 11 using coins {1, 5, 6}. Which approach is best and why?

hard

A. Dynamic Programming, because greedy fails due to no greedy choice property

B. Greedy, because coins are sorted ascending

C. Greedy, because picking largest coin first always works

D. Neither, because problem is unsolvable

Solution

1. Step 1: Check greedy choice property for coin set

   For coins {1,5,6}, greedy picks 6 then 5, total 2 coins for 11, which is minimal. However, greedy does not always work for all amounts with these coins.

2. Step 2: Consider counterexamples and DP suitability

   In some cases greedy fails (e.g., amount 7 with coins {1,5,6} greedy picks 6+1=2 coins but better is 1+1+5=3 coins, so greedy works here but not always. DP always finds minimal coins by exploring all subproblems.

3. Final Answer:

   Dynamic Programming, because greedy fails due to no greedy choice property -> Option A

4. Quick Check:

   Use DP if greedy choice property missing [OK]

Hint: Use DP if greedy choice property is missing [OK]

Common Mistakes:

• Assuming greedy always works for coin change
• Ignoring overlapping subproblems in coin change
• Choosing greedy because coins are sorted ascending