01 Knapsack Problem
Why does it have 0 and 1 in the name?
The “01” in the 01 Knapsack problem refers to the constraint that items can either be included (1) or excluded (0) from the knapsack; they can’t be broken into smaller parts. This differentiates it from other variations like the Fractional Knapsack problem, where you can include fractions of items.
In the 01 Knapsack problem, you have a set of items, each with a weight and a value, and a knapsack with a maximum weight capacity. The goal is to maximize the total value of items in the knapsack without exceeding its weight capacity. For each item, you have two choices: either include it in the knapsack (represented by 1) or exclude it (represented by 0).
So, the “01” signifies the binary decisionmaking process for each item: it’s either in the knapsack or it’s not.
What is the subproblem that we solve in this problem?
In the 01 Knapsack problem, the subproblem usually involves determining the maximum value that can be achieved with a smaller, knapsack of a certain weight capacity, using a subset of the available items. Specifically, for a subknapsack with a weight limit W
and considering only the first i
items, what’s the maximum value that can be obtained?
By solving these subproblems, you build up the solutions to larger problems. This is a classic example of a dynamic programming approach. You store the solutions to subproblems in a table, usually a 2D array, where the entry at [i][w]
represents the maximum value that can be achieved using the first i
items and a knapsack of weight capacity w
.
To solve the overall problem, you’ll find the value at the table entry that considers all items and the full weight capacity of the knapsack. This value is built upon the solutions to the smaller subproblems.
Why do we use a 2D table in this problem?
A 2D table is used in the 01 Knapsack problem to store solutions to subproblems, each identified by two variables: the number of items considered (i
) and the weight capacity of the subknapsack (w
). This allows for efficient lookup and avoids redundant calculations.
Here’s the role of each dimension:
Number of Items (
i
): One dimension of the table tracks the items from 1 ton
, wheren
is the total number of items. This helps in considering all combinations of items.Weight Capacity (
w
): The other dimension represents the possible weight capacities of the knapsack, from 0 to the maximum weight capacityW
. This helps in considering all subknapsacks of varying capacities.
By using a 2D table, you can easily refer to previously solved subproblems when solving the current subproblem. Specifically, the entry at [i][w]
would represent the maximum value achievable by considering the first i
items and a subknapsack of weight capacity w
.
The 2D table allows for a systematic way to fill in values based on the choices you have: either include the current item in the subknapsack or exclude it. This structure makes the dynamic programming approach efficient and straightforward to implement.
How did we identify these two variables are needed ?
The identification of the two variables—number of items (i
) and weight capacity (w
)—comes from analyzing the nature of the problem and what changes as you try to solve it. In the 01 Knapsack problem, you’re dealing with a set of items, each with a weight and value, and a knapsack with a weight capacity. These elements give rise to two key questions:
Which items are under consideration?: As you progress through the problem, you make decisions about whether to include or exclude each item. Therefore, the “number of items considered” becomes an essential variable that helps define each subproblem.
How much weight can the knapsack hold?: The weight capacity of the knapsack is another natural constraint. The “current weight capacity” of the subknapsack also defines each subproblem as you have to respect this constraint while maximizing value.
These two questions align with the two variables, and they capture the essence of what changes from one subproblem to another. By recognizing these variables, you can divide the original problem into manageable subproblems. This makes it possible to use dynamic programming to solve the 01 Knapsack problem efficiently.
What is the brute force solution for this problem?
The bruteforce solution to the 01 Knapsack problem involves generating all possible combinations of items that can be put into the knapsack and then selecting the combination that has the maximum total value without exceeding the weight capacity.
Steps:
Generate Combinations: For
n
items, generate all 2^n possible combinations. Each item can either be included (1) or excluded (0), leading to a binary representation for each combination.Check Weight: For each combination, sum up the total weight of the items included. Discard combinations that exceed the knapsack’s weight capacity (
W
).Calculate Value: For each valid combination (i.e., those that don’t exceed
W
), sum up the total value of the items included.Select Maximum: Among all valid combinations, choose the one with the maximum total value.
Complexity:
 Time Complexity: The number of combinations is 2^n, so the time complexity is O(2^n).
 Space Complexity: O(n) for storing the items and their properties (weight, value).
The bruteforce method is straightforward but highly inefficient, especially as the number of items (n
) increases. This is why more efficient methods, like dynamic programming, are generally preferred for solving this problem.
Explain how the time complexity is 2^n
The time complexity of the bruteforce solution for the 01 Knapsack problem is O(2^n) because you explore all possible combinations of including or excluding each item in the knapsack. Here’s a breakdown of why that’s the case:
Two Choices: For each item, you have two choices: either include it in the knapsack (1) or exclude it (0).
Number of Items: If you have
n
items, then for the first item you have 2 choices, for the second item another 2 choices, and so on, up to the nth item.Total Combinations: To find the total number of combinations, you multiply the number of choices together: 2 * 2 * 2 * … (n times) = 2^n.
Each of these 2^n combinations represents a unique way to fill the knapsack, and you have to evaluate each one to find the combination that maximizes value without exceeding weight capacity.
So, the total number of operations you need to perform is proportional to 2^n, leading to a time complexity of O(2^n).
How can I visualize this process?
Visualizing the bruteforce approach to the 01 Knapsack problem can be done effectively using a tree diagram, often called a “decision tree.”
Decision Tree:
Root: Start with a root node representing the empty knapsack (total weight = 0, total value = 0).
Branches: For each item, create two branches:
 One for including the item in the knapsack (marked as 1).
 One for excluding the item from the knapsack (marked as 0).
Nodes: Each node in the tree will contain:
 The total weight of the knapsack if the items in the path from the root to that node are included.
 The total value of the knapsack if the items in the path from the root to that node are included.
Leaves: The leaf nodes represent all possible combinations of items. Check the total weight and value at each leaf and identify the one with the maximum value that doesn’t exceed the weight limit.
Here’s a simplified example with 3 items (A, B, C):
(Root: W=0, V=0)
/ \
(Include A) (Exclude A)
/ \ / \
(Include B) (Exclude B) ...
/ \ / \
(Include C) (Exclude C) ...
Each level of the tree corresponds to a decision about one item, and you traverse this tree to explore all 2^n possible combinations. In this example, there would be 2^3 = 8 leaf nodes representing all the combinations of including or excluding the 3 items.
This tree visualization provides a graphical understanding of why the time complexity is O(2^n): each item doubles the number of possible combinations, leading to a total of 2^n combinations for n
items.
Why do we have 0 for both state variables to start with?
In the dynamic programming solution for the 01 Knapsack problem, having a “0” state for both variables—number of items considered (i
) and weight capacity (w
)—serves as the base case for the subproblems. Here’s why:
Number of Items (i=0): When no items are considered, the maximum value you can achieve is zero, regardless of the knapsack’s weight capacity. This forms the base for calculations when you start including items.
Weight Capacity (w=0): When the weight capacity of the knapsack is zero, you can’t include any items, and the maximum value is zero. This is true regardless of how many or what kinds of items are available.
Role in Dynamic Programming:
Initialization: These zero states provide initial values for filling in the 2D table. The first row and the first column are usually initialized to zeros, serving as starting points for solving subproblems.
Building Solutions: As you fill in the table, you often refer back to these zero states. For example, if you’re considering whether to include an item in a subknapsack, one of the options is to exclude it, effectively falling back to the solution for the subknapsack with the same weight but one fewer item (i.e.,
i1
).Optimal Substructure: These base cases align with the concept of optimal substructure in dynamic programming, where the solution to a problem is composed of optimal solutions to its subproblems.
Starting with “0” for both variables ensures that all potential subproblems are covered, providing a comprehensive and accurate solution to the overall problem.
What will happen if we don’t have 0 as the initial values for both variables?
If you don’t start with 0 as the initial values for both variables—number of items considered (i
) and weight capacity (w
)—in the 2D table for dynamic programming, you’ll encounter several issues:
Missing Base Cases: Every recursive problem requires base cases for termination. In the 01 Knapsack problem, a weight capacity of 0 and 0 items are natural base cases that indicate you can’t add any value to the knapsack. Missing these can result in incomplete or incorrect solutions.
Incomplete Table: The DP table starts with these zero states to build up solutions for more complex subproblems. Without starting at zero, you miss the foundational steps required to accurately fill in the rest of the table.
Erroneous References: The DP solution relies on referring to previously solved subproblems. If the table doesn’t start at zero, you may end up referring to undefined or incorrect states, leading to incorrect solutions.
Complications in Code: You would have to handle edge cases separately in your code, complicating the logic. The clean, elegant dynamic programming approach becomes harder to implement and maintain.
Reduced Efficiency: Dynamic programming aims to avoid redundant calculations by storing and reusing previously computed results. If you don’t start with zeros, you risk performing redundant calculations for basic cases, which can decrease the efficiency of your algorithm.
In summary, not starting with 0 for both state variables disrupts the logical and computational flow of the dynamic programming approach, leading to incorrect or incomplete solutions and less efficient code.
In recursion we use base cases to stop the recursion when we are done, however in DP, we can implement the algorithm iteratively, so why do we need the base cases?
In dynamic programming (DP), base cases serve a different purpose than merely stopping recursion, as they would in a purely recursive solution. Here’s why base cases are still important in an iterative DP implementation:
Starting Point: In an iterative DP approach, base cases provide the initial values that you start building upon. They serve as the foundation for solving more complex subproblems.
State Transitions: As you fill in the DP table, each entry typically depends on one or more previous entries. Base cases offer those ‘initial’ states that other states refer to when being calculated. Without them, the table can’t be filled in a consistent or meaningful way.
Logical Completeness: Base cases encapsulate scenarios where the problem has trivial or welldefined answers. They make the DP algorithm logically complete and correct, ensuring that all edge cases are appropriately handled.
Problem Decomposition: DP involves breaking down a complex problem into simpler subproblems. Base cases correspond to the simplest subproblems, providing a clear stopping point for this decomposition process, even in an iterative context.
Code Simplification: Without base cases, you’d have to introduce additional logic to handle what are essentially trivial scenarios, complicating your code and making it more errorprone.
So even in an iterative context, base cases are crucial for the correct and efficient operation of a dynamic programming algorithm. They provide the essential groundwork upon which the entire solution is constructed.
Is base cases also applicable to iterative solutions?
Yes, base cases are applicable to iterative solutions as well. While the context differs from recursive implementations, base cases in iterative algorithms serve some of the same fundamental purposes:
Initialization: Base cases establish the initial state of the problem. In iterative algorithms, they often set up the initial values of variables or arrays.
Loop Control: In some iterative solutions, base cases can determine the starting point or ending point of loops. They help you understand under what conditions you should continue iterating or terminate the loop.
Logical Completeness: Just like in recursive solutions, base cases in iterative methods ensure that all edge cases are handled, making the algorithm logically complete.
Simplification: Having base cases can simplify the main part of the algorithm by allowing you to focus solely on the ‘usual’ cases, confident that edge cases are already taken care of.
Correctness: The base cases act as anchors for the algorithm’s logic, ensuring that the method produces correct results for trivial inputs, which in turn helps to ensure that it works for more complex inputs.
So, whether you’re working with recursive or iterative algorithms, base cases are essential for ensuring correctness, simplification, and logical completeness.
Why is it called a “base” case?
The term “base case” originates from the idea that it serves as the foundation or “base” upon which the rest of the solution is built. In both recursion and dynamic programming, algorithms are designed to break a problem down into smaller, more manageable subproblems. The base case represents the simplest, most elementary form of the problem, where the solution is straightforward and doesn’t require further decomposition.
Foundation: Just as a building needs a strong base to support its structure, an algorithm needs base cases to ensure it functions correctly and terminates as expected.
Termination: In recursion, base cases are essential for terminating the recursive calls. They are the conditions under which the algorithm stops calling itself and starts returning values back up the chain.
Initialization: In dynamic programming, especially in iterative solutions, base cases initialize the problemsolving process. They serve as known starting points for solving more complex subproblems.
Logical Completeness: Base cases complete the logical structure of the algorithm. They define what happens in the simplest scenarios and ensure that all edge cases are accounted for.
The term “base” encapsulates these roles, emphasizing the foundational importance of these simplest cases in the overall algorithm.
Is it correct that base case is the smallest instance of the problem and we know the result for it, so it does not require any computation?
Yes, that’s correct. The base case is often the smallest or simplest instance of a problem for which the solution is known directly, without requiring further decomposition or computation. In both recursive and iterative algorithms, base cases serve as:
Termination Points: In recursion, they stop the chain of recursive calls, ensuring that the algorithm doesn’t run indefinitely.
Initial Values: In dynamic programming, base cases provide the initial values in the DP table from which more complex subproblems are solved.
Logical Anchors: They act as fixed points that ground the algorithm, ensuring it has a welldefined behavior for edge cases.
In essence, base cases are known solutions to the simplest forms of the problem, and they serve as the foundation for constructing solutions to more complex instances.
Why do we consider all rows up to the current row when solving a sub problem?
In the context of the 01 Knapsack problem and similar dynamic programming problems, considering all rows up to the current row when solving a subproblem is essential for the following reasons:
Optimal Substructure: Dynamic programming relies on the principle that the optimal solution to a problem can be constructed from optimal solutions to its subproblems. Each row in the DP table represents solutions to subproblems that involve a subset of the available items. To find the optimal solution for the current subset, you may need to refer back to optimal solutions for previous subsets.
State Transition: Each cell in the current row usually depends on cells in previous rows. For example, deciding whether to include an item in the knapsack might depend on the optimal value achieved when that item was not available (a cell in a previous row).
Comprehensive Solution: Looking at all previous rows ensures you are considering all possible combinations of items that could lead to an optimal solution for the current subproblem.
Data Dependency: In many DP problems, the value in the current cell is calculated based on some sort of “transition” from previous states. These states are typically represented in the rows above the current one. Skipping them would mean missing out on potential optimal solutions.
Incremental Building: Each row in the DP table builds upon the ones before it, incrementally adding complexity. By considering all rows up to the current one, you ensure that all possible ways to reach the current state are accounted for, leading to a correct and complete solution.
So, considering all rows up to the current row ensures that you correctly apply the principles of dynamic programming, resulting in a comprehensive, accurate, and optimal solution.
Keeping Track of Maximum
Is the cell in the table keeping track of the maximum we can take so far in the 2d grid?
Yes, each cell in the 2D grid of the 01 Knapsack problem keeps track of the maximum value you can obtain with a given subset of items and a given weight capacity. Specifically, a cell at position (i, w)
represents the maximum value that can be achieved using the first i
items while staying within a weight limit of w
.
Subset of Items: The row index
i
usually represents the subset of items from 1 toi
that you’re allowed to consider.Weight Capacity: The column index
w
represents the remaining weight capacity of the knapsack.Maximum Value: The value stored in the cell
(i, w)
is the maximum value that can be achieved under those constraints. It’s calculated based on previously computed values in the table and the properties of the current item (its weight and value).Dynamic Updates: As you proceed through the table, each cell is updated to reflect new optimal solutions as more items become available for consideration and as the weight capacity changes.
Solution Building: The process starts from the base cases, and the table gets populated in a way that each cell considers relevant information from previously filled cells to compute the maximum achievable value.
In summary, each cell serves as a sort of “memory” that holds the best solution found so far for a specific subproblem defined by the subset of items and the remaining weight capacity.
Tracing the Table
How to trace the table to decide the objects chosen? What is the logical reasoning behind this process?
Tracing back through the table to find the items chosen involves a reverse engineering process that starts at the final cell (n, W)
, where n
is the total number of items and W
is the maximum weight capacity of the knapsack. The value at this cell represents the maximum value that can be achieved. Here’s how to do it and the logical reasoning behind each step:
Starting Point: Begin at the last cell
(n, W)
.Compare With Previous Row: Look at the cell immediately above it,
(n1, W)
. If the value in(n, W)
is the same as the value in(n1, W)
, this means the last item was not included in the optimal solution. Move up a row and repeat this step.Item Included: If the value in
(n, W)
is greater than the value in(n1, W)
, it means the last item was included in the optimal solution. Note this item down.Move Diagonally: Subtract the weight of the included item from the remaining capacity,
W
, and move to the corresponding cell in the previous row,(n1, W  weight of nth item)
. This cell represents the maximum value achievable with the remaining weight after including the nth item.Iterate: Repeat steps 24 until you reach the first row or run out of remaining capacity.
Result: The items noted down during this process are the ones included in the optimal solution.
Logical Reasoning:
Optimal Substructure: The idea is to deconstruct the final solution into optimal solutions of its subproblems. Since DP solutions are built upon optimal solutions to smaller problems, tracing back through the table allows us to reconstruct the optimal solution.
State Transition: Each move, whether vertical or diagonal, represents a decision to either exclude or include an item in the knapsack. This reflects the decisionmaking process that led to the optimal solution.
Item Inclusion/Exclusion: If an item is included in the optimal solution, its value would contribute to the maximum value achievable, resulting in a larger value in the cell compared to the cell immediately above it.
By following these steps and understanding the logical reasoning behind them, you can trace back through the DP table to find out which items were chosen to achieve the maximum value.
Decision Tree
If we draw a decision tree, the cells we pick can be identified in the tree, it will be easier to visualize and reason why we are tracing the path? Is my understanding correct?
Yes, your understanding is correct. A decision tree provides a visual framework for understanding the different choices made at each step in the 01 Knapsack problem. Each node in the decision tree represents a state, and each edge represents a decision to either include or exclude a particular item.
Leaf Nodes: These represent the base cases or final states that are the result of a series of include/exclude decisions. The value at a leaf node will correspond to the total value of the items included along the path from the root to that leaf.
Internal Nodes: These represent intermediate states and correspond to cells in the DP table. Each internal node would have two child nodes: one for the scenario where the current item is included, and one where it is excluded.
Path Tracing: When you trace back through the DP table to find the items that make up the optimal solution, you are effectively tracing a path from a leaf node back to the root in the decision tree.
Visual Aid: The tree visually isolates each decision, making it easier to understand why certain items are included or excluded. Each path in the tree represents a unique combination of items, and the DP table essentially stores the maximum value achievable for each of these combinations (or subproblems).
Efficiency: While the decision tree helps in visualization, keep in mind that it would be inefficient to actually build this tree for large problems. The DP table provides the same information but in a much more storage and timeefficient manner.
So yes, visualizing the process as a decision tree can make it easier to reason about how and why the algorithm chooses specific items for the optimal solution. It provides a clear graphical representation of the problem’s decision space.
Why DP ?
Why is DP chosen for 01 Knapsack, what are the characteristics of this problem that lead to DP approach?
Dynamic Programming (DP) is chosen for the 01 Knapsack problem due to several characteristics that make the DP approach efficient and effective. These characteristics include:
Optimal Substructure: The 01 Knapsack problem exhibits optimal substructure, meaning that the optimal solution to the problem can be constructed from optimal solutions to its subproblems. This is a core property that makes DP applicable.
Overlapping Subproblems: The Knapsack problem involves solving the same subproblems multiple times. For example, finding the maximum value for a smaller weight limit is a subproblem that recurs frequently. DP helps in storing the results of these subproblems to avoid redundant computations.
Finite Number of States: There are a finite number of items and weight capacities to consider, making it possible to represent the problem as a table where each cell corresponds to a subproblem.
BottomUp Computation: The DP approach starts by solving smaller subproblems first and uses their solutions to solve larger subproblems. This is highly efficient and avoids the computational overhead of recursion.
Memoization/Table Filling: In the DP approach, you fill out a 2D table where each cell
(i, w)
represents the maximum value that can be achieved with a subset of the firsti
items and a maximum weight ofw
. Once this table is complete, the final cell contains the solution to the original problem.Polynomial Time Complexity: The bruteforce approach to the Knapsack problem has exponential time complexity (O(2^n)). DP reduces this to polynomial time (O(nW), where
n
is the number of items andW
is the maximum weight capacity), which is significantly faster for large instances of the problem.Deterministic Transitions: The state transitions in the 01 Knapsack problem are deterministic and can be defined precisely, another hallmark of problems wellsuited for a DP approach.
Clear Base Cases: The problem has welldefined base cases (e.g., zero items or zero capacity), which provide a starting point for the DP table.
Because of these characteristics, Dynamic Programming is not just a viable but also an efficient approach to solving the 01 Knapsack problem. It captures the complexity of the problem in a structured manner, reduces time complexity, and provides an optimal solution.
How to recognize the optimal substructure for this problem?
Recognizing optimal substructure in a problem like the 01 Knapsack usually involves breaking down the main problem into smaller subproblems and observing how their solutions relate to the solution of the larger problem. Here’s how you can identify the optimal substructure in the 01 Knapsack problem:
Divide into Smaller Problems: Consider a reduced version of the problem with fewer items and/or a smaller knapsack capacity. Notice how solving these smaller problems can contribute to solving the bigger problem.
InclusionExclusion Principle: For each item, you have two choices: include it in the knapsack or exclude it. If including an item produces an optimal solution for a subproblem, then this optimal solution is part of the optimal solution for the bigger problem.
Transitive Optimality: If you have an optimal solution for a smaller knapsack problem (say, using items 1 to
i
and weightW
), adding one more item (i+1
) should either preserve optimality (if the item is not added) or create a new optimal solution (if the item is added and doesn’t exceed the weight limit).Subproblem Interdependence: Realize that the solution to a subproblem (e.g., maximum value for weight
W
andi
items) is dependent on solutions to related subproblems (e.g., maximum value for weightW  weight[i]
andi1
items).Solve and Combine: Check if the optimal solution to the main problem can be constructed by combining the optimal solutions of its subproblems. If it can, then the problem has an optimal substructure.
Recurrence Relation: If you can express the value of a state (a specific subproblem) based on the values of some other states (other subproblems), that’s a strong indication of optimal substructure. For 01 Knapsack, the relation can be defined as:
dp[i][w] = max(dp[i1][w], dp[i1][w  weight[i]] + value[i])
This relation clearly shows that the optimal solution for a problem depends on the optimal solutions of its subproblems.
Consistency: Observe that solving smaller instances of the problem in an optimal way consistently leads to an optimal solution for larger instances. This consistency is a hallmark of optimal substructure.
Analytical Reasoning: Sometimes, simple logical reasoning can help identify optimal substructure. For instance, if you can logically argue that no solution to a subproblem can be improved without making the overall problem solution worse, then that’s a sign of optimal substructure.
By carefully observing these aspects, you can identify whether a problem like the 01 Knapsack has an optimal substructure, making it a good candidate for a Dynamic Programming approach.
What does “recurrence” in Recurrence Relation mean?
The term “recurrence” in “recurrence relation” refers to the repetitive or recursive nature of the problem. In this context, “recurrence” signifies that the solution to a problem is repeatedly defined in terms of smaller instances of the same problem. The relation provides a way to express the value of a function (or state) based on the values of smaller instances (or preceding states) of that function. This repetition or “recurrence” is what gives the relation its name.
In mathematical terms, a recurrence relation for a sequence ( {a_n} ) is an equation that expresses ( a_n ) in terms of one or more earlier terms in the sequence, ( a_{n1}, a_{n2}, \ldots, a_1 ). For example, the Fibonacci sequence is often defined by the recurrence relation ( F(n) = F(n1) + F(n2) ), with base cases ( F(0) = 0 ) and ( F(1) = 1 ).
In the context of Dynamic Programming, the recurrence relation serves as the foundational equation upon which the entire solution is built. It defines how to break the problem down into smaller subproblems, and how to combine these smaller solutions to form the solution to the original problem. This recurrent breakdown and combination are key features of problems that possess optimal substructure, which is essential for applying Dynamic Programming effectively.
Language Agnostic Coding Drills
The 01 Knapsack problem is a classic problem in computer science and is a perfect example of a problem that can be solved using dynamic programming. Here are the key concepts involved and potential corresponding drills:
Problem Understanding: Understand what the 01 Knapsack problem is.
 Drill: Write a problem statement for the 01 Knapsack problem. It involves a knapsack with a maximum weight capacity and a set of items each with a weight and a value. The goal is to maximize the total value of items in the knapsack without exceeding the weight capacity.
Basic Control Structures: Understand conditional (ifelse) statements and loops.
 Drill: Write a program that reads a series of numbers and prints the largest one.
Arrays: Learn how to create and manipulate arrays (lists in some languages).
 Drill: Create an array of numbers and write functions to find the sum and the maximum value.
2D Arrays: Understand how to work with 2D arrays or matrix.
 Drill: Implement a simple matrix multiplication algorithm using nested loops.
Recursion: Understand how recursion works, since a naive solution to the 01 Knapsack problem can be implemented using recursion.
 Drill: Implement a recursive function to calculate the factorial of a number.
Understanding the Concept of Dynamic Programming (DP): Dynamic programming is used to improve the time complexity of the naive recursive solution.
 Drill: Write a dynamic programming solution for the Fibonacci series, which is simpler than the knapsack problem but still demonstrates the key concepts of dynamic programming.
DP with 1D Arrays: Some dynamic programming problems only need a 1D array, which can be simpler than 2D.
 Drill: Write a dynamic programming solution for the coin change problem, which involves finding the minimum number of coins needed to make a certain amount of change.
DP with 2D Arrays: Understand dynamic programming in the context of 2D arrays, which is needed for the 01 Knapsack problem.
 Drill: Write a dynamic programming solution for the longest common subsequence problem, which involves finding the longest sequence that two sequences have in common.
Understanding and Writing Pseudo Code: Before diving into coding the 01 Knapsack problem, one must understand how to break down the problem and write pseudo code.
 Drill: Write a pseudo code for the 01 Knapsack problem.
Coding the Final 01 Knapsack Solution: Combine all the learned concepts to code the final solution.
 Drill: Implement the 01 Knapsack problem solution using dynamic programming.
Each of these drills corresponds to a concept that is crucial for understanding and implementing the 01 Knapsack problem, and they gradually increase in difficulty and complexity. Starting from basic concepts like control structures and arrays, they gradually build up to dynamic programming with 1D and 2D arrays, before finally tackling the 01 Knapsack problem itself.
Naive Approach
How is naive recursive solution time complexity is O(2n ) in 01 Knapsack problem?
A simple solution is to consider all subsets of items and calculate the total weight and value of all subsets. Consider the only subsets whose total weight is smaller than W. From all such subsets, pick the maximum value subset.
Optimal Substructure
To consider all subsets of items, there can be two cases for every item.
 Case 1: The item is included in the optimal subset.
 Case 2: The item is not included in the optimal set.
Therefore, the maximum value that can be obtained from ’n’ items is the max of the following two values.
 Maximum value obtained by n1 items and W weight (excluding n th item).
 Value of nth item plus maximum value obtained by n1 items and W minus the weight of the nth item (including nth item).
If the weight of ’nth’ item is greater than ‘W’, then the nth item cannot be included and Case 1 is the only possibility.
Implementation


OUTPUT
220
The Java code is a recursive implementation of the 01 Knapsack problem. The following concepts are covered in this code:
Understanding Basic Functionality: The ability to write functions and understanding how they are called is a fundamental concept. In this code,
max
andknapSack
are two methods defined in theKnapsack
class.Defining Conditional Statements: The implementation of
if
,else
and conditional operators to dictate the flow of the program.Understanding and Utilizing Arrays: The ability to define, initialize, and use arrays. In this case,
wt
andval
are arrays representing the weights and values of the items respectively.Recursion: Understanding the recursive nature of the problem and how to implement recursive function calls. This is visible in the
knapSack
function.Base Case of Recursion: The ability to identify and handle the base case in a recursive problem is crucial. In this case, the base case is when
n == 0
orW == 0
.Recursive Case and Decision Making: The recursive case involves deciding whether to include or exclude an item based on its weight and the remaining capacity of the knapsack.
Understanding and Using a Driver Function: A ‘main’ method that serves as the entry point for the program, and is responsible for testing the functionality of other methods.
Printing to the Console: Using the
System.out.println()
method to print results to the console.Java Class Structure: Understanding how to structure a Java class, including defining methods and the main driver function within the class.
Using Function Return Values: Understanding how to use the returned value from a function call. In this case, the
knapSack
andmax
functions both return values which are used in further computations.
The function computes the same subproblems again and again. See the following recursion tree, K(1, 1) is being evaluated twice. The time complexity of this naive recursive solution is exponential (n).
In the following recursion tree, K() refers to knapSack().
The two parameters indicated in the following recursion tree are n and W.
The recursion tree is for following sample inputs.
wt[] = {1, 1, 1}, W = 2, val[] = {10, 20, 30}
K(n, W)
K(3, 2)
/ \
/ \
K(2, 2) K(2, 1)
/ \ / \
/ \ / \
K(1, 2) K(1, 1) K(1, 1) K(1, 0)
/ \ / \ / \
/ \ / \ / \
K(0, 2) K(0, 1) K(0, 1) K(0, 0) K(0, 1) K(0, 0)
Recursion tree for Knapsack capacity 2 units and 3 items of 1 unit weight.
Complexity Analysis
Time Complexity: O(2n). As there are redundant subproblems.
Auxiliary Space :O(1).
As no extra data structure has been used for storing values.
Language Agnostic Coding Drills
Here’s the breakdown of the 01 Knapsack problem solution into smallest units of learning, arranged in increasing level of difficulty:
Understanding and Implementing Functions/Methods: Learn how to define functions, how to call them, and how to return values from them.
Working with Arrays: Learn to declare and initialize arrays, and how to access their elements.
Using Conditional Statements: Master the use of
ifelse
statements to control the flow of your program.Implementing a ‘Maximum’ function: This is a simple use of conditional statements. Given two numbers, write a function that returns the larger number.
Implementing Recursive Functions: Learn the basics of recursion: how to define a base case, and how to write the recursive case.
Understanding and Implementing the Concept of 01 Knapsack: Learn the logic of the 01 knapsack problem and translate that logic into code. This involves deciding, for each item, whether to include it in the knapsack or not, based on its value and weight, and the remaining capacity of the knapsack.
Implementing Base and Recursive Cases for the Knapsack Problem: The base case for this problem is when there are no items left to consider or when the capacity of the knapsack is 0. The recursive cases deal with the decision to include or exclude an item, and how that decision impacts the remaining capacity of the knapsack and the remaining items to consider.
Dealing with Edge Cases: For the 01 Knapsack problem, an edge case arises when the weight of an item exceeds the current capacity of the knapsack. This requires a specific branch in the code logic.
Understanding and Implementing a Driver Function: In this function, the input data is prepared and the main function is called. Then the result is printed or returned.
Testing the Code: Finally, learning to write tests for the implemented code to make sure it’s working as expected.
When each of these concepts is mastered individually, they can be combined to create the final solution for the 01 Knapsack problem.
Solution for Coding Drills in Python
Here are the Python implementations corresponding to each of the learning units:
 Understanding and Implementing Functions/Methods


 Working with Arrays


 Using Conditional Statements


 Implementing a ‘Maximum’ function


 Implementing Recursive Functions


Understanding and Implementing the Concept of 01 Knapsack (This is a conceptual drill and doesn’t require a coded solution.)
Implementing Base and Recursive Cases for the Knapsack Problem


Dealing with Edge Cases (This is handled within the recursive cases in the knapSack function above.)
Understanding and Implementing a Driver Function


 Testing the Code


These are standalone pieces of code related to the individual concepts. In a complete program, they will have to be properly combined and structured.