Using Constraints to Solve Algorithmic Problems
Many computational problems can be modeled and solved using constraints. The key ideas are:
 Identify the problem variables and domains
 Express the requirements and goals as constraints over the variables
 Combine search techniques with constraint propagation and reasoning to find solutions
For example, Sudoku can be formulated as:
Variables:
Cell values x1...x81 ∈ {1..9}
Constraints:
 Each row contains 1..9
 Each column contains 1..9
 Each 3x3 box contains 1..9
 Cells match given values
Goal:
Find variable assignment satisfying constraints
Solving involves propagation techniques like:
 Domain reduction  Remove values for cell that violate constraints
 Forward checking  Propagate constraints from assigned cells
 Inference  Deduce new constraints from existing ones
Combined with search, constraint solving explores possible assignments while pruning infeasible branches.
Constraints provide a declarative way to model requirements separate from control flow. This simplifies problem specification for the solver. Useful for scheduling, planning, design, and combinatorial problems.
Constraints are essential in understanding and solving algorithmic problems. They help to identify the limits within which the problem has to be solved and often suggest efficient strategies for solving the problem.
How to Use Constraints
Here’s how you can use constraints to solve algorithmic problems:
Identifying Appropriate Data Structures: The constraints on the size and nature of the data can guide you in selecting the right data structures. For instance, if the input size is large, you might opt for data structures with efficient query times, like hash maps or balanced binary search trees.
Choosing the Right Algorithm: The problem constraints can often help identify the most efficient algorithm to use. For example, if the input array is already sorted, you can use binary search instead of linear search, improving time complexity from O(n) to O(log n).
Time and Space Complexity: Constraints related to time and memory limits guide us in developing a solution that is efficient in both time and space. For instance, if you have a tight time limit and a large input size, you would need to avoid algorithms with high time complexity, like O(n^2), and aim for linear or logarithmic time complexities.
Edge Cases and Assumptions: Constraints can also help identify edge cases that need to be handled in the solution. For example, if a constraint states that numbers in a list can be negative, you have to take that into account while writing your algorithm.
Optimization Techniques: Constraints can also hint at specific optimization techniques, like dynamic programming or greedy algorithms. For example, problems that involve finding an optimal solution and have overlapping subproblems and optimal substructure often indicate that dynamic programming might be a useful approach.
In summary, constraints help frame the problem and guide the selection of algorithms and data structures to create efficient, effective solutions.
Difficulty in the Problem
What is it about the problem that makes it hard?
Then, make the difficulty disappear. You may not be able to do this legally. Temporarily avoiding the hard part of a problem will allow you to make progress and may shed light on the difficulties. For example, if the problem involves big numbers, make them small.
If a problem involves complicated constraints, try looking at a similar problem without such constraints. At best, pretending that the difficulty isn’t there will lead to a bold solution. At worst, you will be forced to focus on the key difficulty of your problem and possibly formulate an intermediate question, whose answer will help you with the problem at hand.
This principle is related to problemsolving techniques like simplification, abstraction, and the method of “working backwards”. Here’s how it might work:
Simplification: If you’re faced with a complicated problem, one approach is to simplify it. This can involve reducing the size or complexity of the problem, or temporarily ignoring some of the constraints or requirements. By solving this simpler version, you can gain insights or methods that help solve the original, more complex problem.
For example, suppose you’re asked to write an algorithm that sorts a large list of strings in alphabetical order, and handles a range of edge cases (like punctuation, capitalization, and special characters). A first step could be to write an algorithm that just sorts a short list of simple, lowercase words with no special characters. Once you’ve done that, you can gradually add complexity back in.
Abstraction: Another approach is to abstract the problem, focusing on the underlying structure or patterns rather than the specific details. This can make the problem easier to understand and solve, and can often reveal deeper insights or more general solutions.
For instance, if you’re asked to solve a complex problem involving a specific business process, you might start by abstracting the problem into a common pattern (like a graph traversal or a search problem) and solve that. You can then map this solution back onto the original problem.
Working Backwards: If a problem seems too complex to solve directly, another approach is to start with the desired outcome and work backwards to the starting point. This can often clarify what steps or methods are needed.
For example, if you’re asked to design an algorithm that can navigate a complex network, you might start by looking at the desired end point and working backwards to the starting point, identifying the decision points and constraints along the way.
Intermediate Questions: In some cases, solving a simplified or abstract version of the problem, or working backwards from the end result, can lead to intermediate questions that are easier to answer but still help to solve the original problem.
For instance, if you’re trying to design a system that can handle a large amount of data, you might start by asking how to handle a smaller amount of data, or how to handle a large amount of simpler data. Answering these questions can lead to methods or insights that help solve the original problem.
In all of these techniques, the key is to reduce the complexity of the problem, solve this simpler problem, and then apply the insights or methods gained to the original, more complex problem.
The principle is essentially a combination of several problemsolving strategies, and doesn’t have a single universallyaccepted name. However, it’s often associated with terms like “problem simplification”, “problem abstraction”, “working backwards”, and “dividing and conquering”.
This principle embodies the essence of “Divide and Conquer” strategy, where a complex problem is divided into simpler subproblems, and the solutions of these subproblems are combined to form the solution of the original problem.
The idea of reducing complexity, handling smaller or simplified instances, and then generalizing back to the original problem is also similar to the concept of “inductive reasoning” in mathematics and computer science.
This approach is also related to the concept of “stepwise refinement”, a methodology in software engineering where you start with a highlevel algorithm and incrementally break it down into more detailed steps.
The principle overall highlights the importance of breaking down complexity, understanding the essence of the problem, and tackling manageable pieces that progressively lead to the full solution.
Example
Extract constraints from the problem statement. Ask clarifying questions to find constraints. Constraints are limitations or restrictions placed on the program that you are constructing. Search for constraints by asking yourself the questions:
 What are the limitations or restrictions?
 What is the range of inputs that the program must handle?
 What is the lower bound?
 What is the higher bound?
 Do we need to solve the problem with a given time and space complexity?
 Why identify constraints?
We can make simplifying assumptions that will make the code concise. We will solve the given problem instead of making invalid assumptions about the solution. Extracting constraints from the problem statement requires asking the right clarification questions. At this point, you might have questions such as:
 How to identify the constraints?
 What happens when you don’t find them as early as possible?
 How to express the constraints in code?
Discuss the answers to these questions and work through coding problem as an example.
What are constraints?
In the context of algorithmic problemsolving, constraints are rules or limitations on the solutions to a problem. They define the conditions that a solution must meet and often pertain to aspects like the input’s size, memory usage, or running time. Constraints can be explicit, mentioned directly in the problem statement, or implicit, implied by the problem’s context.
For example, in a sorting problem, a common constraint is that the output must be in nondecreasing order. If you are implementing a hash table, a constraint might be that no two items can have the same key.
Why identify constraints?
Identifying constraints is crucial for several reasons:
Efficiency: Knowing the constraints can help you choose the most efficient algorithm or data structure for the problem. For instance, if the input size is small, you can use a simple but slower algorithm. If the input size is large, you’ll need an algorithm that can handle large inputs efficiently.
Correctness: Some constraints are necessary to ensure the correctness of the solution. For instance, if a problem requires finding a solution in a certain time complexity, not meeting this constraint would mean your solution is incorrect.
Understanding: Constraints help us better understand the problem and its context. They help set boundaries on the problem space, guiding our thought process towards a valid solution.
How to identify the constraints?
To identify constraints, you should carefully read the problem statement and ask clarification questions if needed. Consider the following:
Input and Output: Look at the size of the input and what the output should be. This can often provide insight into the time and space constraints.
Time and Space: Most problems will have constraints on time complexity and space complexity. Ensure you understand what these are.
Special Conditions: There may be unique conditions specific to the problem. For example, in a graph problem, a constraint might be that the graph is acyclic or that the graph is fully connected.
What happens when you don’t find constraints as early as possible?
Failing to identify the constraints as early as possible can lead to incorrect solutions or solutions that are not optimal. You might end up with a solution that works for some cases but fails for others that fall within the constraints.
How to express the constraints in code? Expressing constraints in code can vary greatly depending on the problem and the language you are using. It often involves using conditional statements to handle different cases based on the constraints.
For example, in a sorting problem where a constraint is that the array length is less than or equal to 10^3, you could use a simple sorting algorithm like bubble sort. You might express this in code like:


In this case, if the array length is within the specified constraint, a simpler (but generally slower) sorting algorithm is used. If the array length exceeds this constraint, a more efficient sorting algorithm is chosen.
Constraints guide the way we approach a problem and inform our decisions when it comes to choosing the best strategy, algorithm, or data structure to solve it. They are critical in algorithmic problemsolving and should be given ample consideration when studying a new problem.