Searching the Problem Space
Can we search the problem space and find cues in the problem statement such that we can come up with the right approach to solve a given algorithmic problem?
Analyzing the problem statement is the first step in problemsolving and it often holds cues to the right approach. Here’s how:
Understand the Problem: The first step is to understand the problem thoroughly. Read the problem statement multiple times, understand what input is given, and what output is expected.
Identify Key Words: Certain words or phrases in problem statements can hint at specific algorithms or data structures. For example, “shortest path” can point to graph algorithms like Dijkstra’s or BellmanFord. “Subsequence” or “subset” often indicates a dynamic programming problem.
Look at the Constraints: The constraints of the problem often help you to determine the possible approaches to solve it. The size of the input can often indicate the time complexity required for the solution.
Identify the Problem Type: Many problems fit into certain categories such as search problems, optimization problems, combinatorial problems, etc. Identifying the problem type can lead you to a set of triedandtested approaches.
Try Examples: Create a few examples and work through them. This can often help you understand the problem better and might hint at the underlying algorithm.
Look for Patterns: If you work out a few examples, sometimes you can spot a pattern or a rule that you can use to build your algorithm.
Break It Down: If the problem seems complex, try breaking it down into smaller, simpler problems. Solving these can often give you insight into solving the overall problem.
Remember, this is more of an art than a science. With practice, you can get better at recognizing these cues and using them to guide your problemsolving process.
Here’s a table with some cues and corresponding approaches in computer science problemsolving:
Cue  Approach 

Shortest path  Graph algorithms (Dijkstra’s, BellmanFord) 
Longest path  Graph algorithms (DFS, BellmanFord) 
Subsequence  Dynamic Programming 
Subset  Dynamic Programming / Backtracking 
Maximum, minimum  Greedy algorithms 
Most, least, fewest, greatest  Greedy algorithms/ Priority Queues 
Permutations  Backtracking / Recursion 
Combinations  Backtracking / Recursion 
Sorted sequence  Binary Search / Sorting Algorithms 
Distinct elements  Set / Map / Hashing 
Frequently occurring item  Hashing / Bucket sort 
Relationship between elements  Graphs / Trees 
Hierarchical order  Trees / Heap data structure 
Sequential order  Arrays / Linked Lists 
Keyvalue pairs  Hashing / Map / Dictionary 
Spatial arrangement  Geometric Algorithms / Scanline Algorithm 
Uncertain decisions  Probabilistic Algorithms 
Fixed order  Stack / Queue data structure 
FirstInFirstOut order  Queue data structure 
LastInFirstOut order  Stack data structure 
Order not important  Combinatorial problem / Heap 
Pairing  Two Pointers / Map 
“Top k”  Heap / Priority Queue 
Intersection/Union  Set / Hash Map 
Balancing  AVL trees / RedBlack trees 
Connectivity  UnionFind / Disjoint set 
Repetition  Loop / Recursion 
Unknown number of steps  Recursion 
Scheduling  Queue / Priority Queue 
This table provides some common cues and their corresponding approaches in problemsolving. However, it’s important to remember that these cues are not definitive. Different problems might have different solutions even if they share similar cues, depending on the exact requirements and constraints. Also, this table doesn’t cover every possible cue or approach, but it’s a good starting point for many common problem types.
These are general guidelines, and not every problem that contains these cues will require the corresponding approach. They’re useful hints, but solving the problem will still require a thorough understanding of the problem domain and the underlying concepts of the approach in question.
Search the Problem Space
A problem space is the set of possibilities generated by any welldefined problem. Many fundamental problem solving tactics involve the search for order. Often problems are hard because they seem chaotic or disorderly. They appear to be missing parts (facts, variables, patterns) or the parts do not seem connected.
Look for clues in the problem statement. Finding and using order can quickly simplify such problems. List the problem solving tactics that help us find or impose order where there seemingly is none.
Problem space refers to the totality of potential actions, pathways, or solutions that could potentially solve a given problem. Understanding the problem space is crucial for effective problemsolving as it can help you identify potential solutions and understand the complexity of the problem.
For example, consider the classic problem of a Rubik’s cube. The problem space here consists of all possible states the cube can be in, and all possible moves that can be made from each state. The aim is to find a sequence of moves that transitions the cube from its current state to a solved state.
Often, problems can seem daunting or chaotic because the problem space is vast, or because it’s unclear how different elements are related.
Finding and Using Order
One of the primary tactics in problemsolving is to search for some form of order or structure within the problem space. This order could be patterns in the data, relationships between variables, constraints that limit the possible solutions, or any other kind of regularity or structure.
For example, in the game of chess, a beginner might view the game as a chaotic and vast problem space, with an almost infinite number of possible sequences of moves. However, an experienced player sees patterns and structures  they recognize common sequences of moves (openings, tactics, etc.), they understand the relative value of pieces, they can predict the outcome of certain exchanges, etc. This understanding helps them to navigate the problem space more effectively.
Tactics for Finding Order
Decomposition: Break down the problem into smaller, more manageable subproblems. Each of these subproblems will have a smaller, simpler problem space.
Pattern recognition: Look for patterns in the problem space. These might be repetitions, symmetries, or any other kind of regularity.
Abstraction: Try to find a more abstract representation of the problem. This can often reveal underlying structure or relationships.
Working backwards: Start from the goal state and try to find a path back to the initial state. This can often simplify the problem space by eliminating irrelevant options.
Constraint identification: Identify constraints in the problem that limit the number of possible solutions. These constraints can often significantly reduce the size of the problem space.
Analogical thinking: Compare the problem to other, similar problems that you’ve already solved. This can often provide insights into potential solutions, or ways to simplify the problem space.
In each case, the goal is to simplify the problem space, and make it easier to find a solution. This is a dynamic process  as you explore the problem space and try different approaches, your understanding of the problem and its structure will evolve.
Claude Explanation
Yes, it is often possible to analyze a problem statement carefully to identify clues that suggest useful approaches for solving algorithmic problems. Here are some strategies for searching the problem space and finding cues:
Look for optimality conditions  If the problem asks for the optimal or minimum/maximum solution, it often suggests dynamic programming or greedy algorithms.
Identify relationships between entities  If the problem involves relationships like precedence, dependency, sorting etc. between entities, it may suggest graph algorithms like topological sort, shortest paths etc.
Spot recursive structures  Problems involving recursive mathematical structures like trees, subsets, permutations/combinations etc. can be solved recursively.
Break down into simpler cases  Decompose the problem into simpler base cases and build up a solution. This suggests recursion or bottomup dynamic programming.
Note repetitions  Repetitive structures or operations indicate iterating over states/solutions. This may need loops, breadthfirst search, etc.
Invert the problem  If the direct problem is hard, look at inverting it. For example, validate vs find violations.
Simplify constraints  Remove complex constraints to get to the core of the problem for insight into solutions.
Relate to known problems  See if the problem resembles any classic algorithms/data structures studied before.
Find analogues  Map the problem into an analogous problem domain like graph theory and leverage known techniques.
With practice, one can develop intuition for mapping problem statements to potential algorithms by carefully analyzing the properties, relationships, structures and patterns within the problem space. The goal is to search the space effectively to uncover clues that lead to productive algorithm design ideas.
Here are coding examples illustrating some of the strategies for searching the problem space to find algorithmic solution cues:
Optimality conditions  Dynamic programming


Relationships between entities  Graph algorithms


Recursive structures  Recursion


Break into simpler cases  Dynamic programming


These examples demonstrate how we can map from properties of the problem statement to data structures and algorithms. The key is to actively search for these cues during problem analysis.
ChatGPT Explanation
Yes, searching the problem space and examining the problem statement closely are often key steps to determining the right approach for solving an algorithmic problem. Here’s how you can go about it:
Analyze the Problem Statement
Identify Constraints: Look for limits on time complexity and space complexity. This can often indicate the kind of algorithmic approach needed.
InputOutput Patterns: Observe if the problem has any specific patterns in the input or output. For example, if the output is a permutation of the input, sorting algorithms might be useful.
Keywords: Words like “maximum”, “minimum”, “longest”, or “shortest” often indicate that optimization techniques like dynamic programming may be applicable.
Boundaries: If the problem set size is small, bruteforce techniques could be sufficient. Conversely, if the data set is large, more efficient algorithms are likely required.
Investigate Characteristics
Order: Does order matter in the problem? If not, you might be able to sort the input or use sets to simplify the problem.
Repetition: Are there repetitive calculations? Memoization or tabulation techniques could be helpful.
Symmetry and Invariance: Is there a form of symmetry or an invariant in the problem? Recognizing these can lead to more efficient solutions by reducing the problem space.
Formulate Approach
Divide and Conquer: Can the problem be broken down into smaller, similar subproblems?
Iterative vs Recursive: Decide whether an iterative or recursive approach is more suitable based on the problem’s nature.
Data Structures: Consider what data structures could help solve the problem more efficiently (e.g., queues, stacks, trees).
Key Takeaways
Constraints: Understanding constraints helps in choosing the correct algorithmic paradigm.
Patterns: Identifying inputoutput patterns can lead you to a more efficient solution.
Characteristics: Features like order, repetition, symmetry, and invariance can be cues to the right approach.
By carefully examining the problem space and cues in the problem statement, you can more effectively choose the appropriate algorithmic approach. This systematic method enhances your problemsolving toolkit, allowing you to tackle complex issues with more precision.