# Recognizing Cues in Problem Statement to Categorize Problem Classes

There are standard ways of ordering information on paper for certain classes of problems. These methods are not always obvious. For instance, for matrix problems, we put the information into a table or matrix. Graph is the visual model for path problems.

## The Art of Organizing Information

When we talk about problem-solving, especially in complex scenarios like algorithmic problem-solving, it’s crucial to present the problem’s information in a way that aids understanding and problem-solving. Different classes of problems lend themselves to different formats of information organization, which can be visual, symbolic, tabular, or a combination thereof.

Consider matrix problems. These are problems that involve multi-dimensional data. A good example could be a problem where we are given a grid (2D array or matrix) representing a map of a city, and we are asked to find the shortest path from one point to another, avoiding certain obstacles. In such a scenario, it makes sense to represent the city map as a matrix on paper, where each cell of the matrix represents a point in the city. This visual organization of data helps us understand the spatial relationship between different points in the city and can guide our approach to the problem.

Similarly, when we are dealing with path problems, we generally use graphs to represent the problem visually. For instance, consider the classic traveling salesman problem, where the goal is to find the shortest possible route that visits a list of cities and returns to the origin city. Here, a graph serves as an excellent visual representation where cities are represented as nodes, and the paths between them are represented as edges. The weights on the edges can represent the distances between cities. This visual model makes the problem easier to comprehend and brings clarity to the solution process.

It’s important to note that these ways of organizing information are not always immediately apparent. It requires a certain familiarity with the problem domain and the experience of having worked with similar problems. With practice and over time, the process of mapping a problem to its visual or symbolic representation becomes more intuitive.

These visual or symbolic models become particularly useful when working with abstract data structures or complex algorithms. They serve as a bridge between the abstract problem space and the concrete solution space, guiding the transition from problem understanding to solution design.

## Categorization of Problems by Problem Domain

Categorizing the class of problems based on the terms and conditions stated in the problem statement (problem domain) can be tricky due to the variability in how problem statements are formulated. However, we can attempt to do so based on the nature of the problems and their typical requirements. Here’s a high-level classification:

**Resource Allocation Problems:**These problems involve assigning limited resources to meet the needs of competing objectives. Terms in the problem statement might include “schedule”, “assign”, “distribute”, “allocate”, or “optimize”.**Sequencing Problems:**These problems require arranging items in a particular order based on certain criteria. Key terms might include “order”, “sequence”, “sort”, or “arrange”.**Pathfinding Problems:**These problems revolve around finding the best path between two points, often within a network or graph. Key terms may include “shortest”, “longest”, “cheapest”, “route”, “path”, “travel”, or “journey”.**Matching Problems:**These problems involve pairing items based on certain criteria. Terms you might encounter include “match”, “pair”, “couple”, or “connect”.**Packing and Covering Problems:**These problems involve the selection of items to fill a space or meet a requirement. Key terms may include “fill”, “pack”, “cover”, or “complete”.**Partitioning Problems:**These problems involve dividing a set of items into subsets. Terms may include “divide”, “split”, “separate”, or “group”.**Selection Problems:**These problems require the selection of a subset of items to meet a condition or maximize a value. Key terms might include “select”, “choose”, “pick”, or “maximize”.**Counting Problems:**These problems involve counting the number of ways something can occur, often under certain conditions. Terms may include “count”, “number of”, “ways”, or “how many”.

Each class of problems could potentially be visualized in different ways based on their specific requirements. For instance, resource allocation problems might be represented with a table or matrix, sequencing problems might be modeled as an ordered list or array, and pathfinding problems might be depicted as a graph or tree.

There are countless ways to categorize problems, depending on the specific domains and contexts in which they appear. In addition to the types I’ve mentioned, here are a few more:

**Prediction Problems:**These are often seen in data science and machine learning, and they involve using data to predict future outcomes. Key terms may include “predict”, “forecast”, “estimate”, or “model”.**Classification Problems:**Another common type in machine learning, these problems involve sorting data into categories. Key terms might include “classify”, “category”, “label”, or “sort”.**Search Problems:**These involve finding specific items within a given data structure or set. Key terms might include “find”, “search”, “locate”, “retrieve”, or “query”.**Decision Problems:**These problems involve making choices to achieve the best outcome, often under constraints. Key terms may include “decide”, “choice”, “option”, “strategy”, or “policy”.**Validation Problems:**These problems require verifying if a certain condition or set of conditions holds true for a given dataset or system. Key terms might include “validate”, “verify”, “check”, or “confirm”.**Comparison Problems:**These problems involve comparing two or more entities based on certain criteria or metrics. Key terms may include “compare”, “contrast”, “difference”, or “similarity”.**Design Problems:**These problems require devising a plan, schematic, or model to fulfill certain criteria. Key terms may include “design”, “plan”, “model”, or “scheme”.

Again, these categories are not mutually exclusive, and many complex problems may fall into multiple categories. Moreover, the strategies for visualizing or solving these problems can vary greatly, depending on the specific details and requirements of each problem.

## Types of Computational Problems and Their Key Terms

Finding Cues in the Problem Statement

No | Problem Type | Explanation | Terms |
---|---|---|---|

1 | Resource Allocation Problems | These problems involve assigning limited resources to meet the needs of competing objectives. | “schedule”, “assign”, “distribute”, “allocate”, “optimize” |

2 | Sequencing Problems | These problems require arranging items in a particular order based on certain criteria. | “order”, “sequence”, “sort”, “arrange” |

3 | Pathfinding Problems | These problems revolve around finding the best path between two points, often within a network or graph. | “shortest”, “longest”, “cheapest”, “route”, “path”, “travel”, “journey” |

4 | Matching Problems | These problems involve pairing items based on certain criteria. | “match”, “pair”, “couple”, “connect” |

5 | Packing and Covering Problems | These problems involve the selection of items to fill a space or meet a requirement. | “fill”, “pack”, “cover”, “complete” |

6 | Partitioning Problems | These problems involve dividing a set of items into subsets. | “divide”, “split”, “separate”, “group” |

7 | Selection Problems | These problems require the selection of a subset of items to meet a condition or maximize a value. | “select”, “choose”, “pick”, “maximize” |

8 | Counting Problems | These problems involve counting the number of ways something can occur, often under certain conditions. | “count”, “number of”, “ways”, “how many” |

9 | Prediction Problems | These are often seen in data science and machine learning, and they involve using data to predict future outcomes. | “predict”, “forecast”, “estimate”, “model” |

10 | Classification Problems | Another common type in machine learning, these problems involve sorting data into categories. | “classify”, “category”, “label”, “sort” |

11 | Search Problems | These involve finding specific items within a given data structure or set. | “find”, “search”, “locate”, “retrieve”, “query” |

12 | Decision Problems | These problems involve making choices to achieve the best outcome, often under constraints. | “decide”, “choice”, “option”, “strategy”, “policy” |

13 | Validation Problems | These problems require verifying if a certain condition or set of conditions holds true for a given dataset or system. | “validate”, “verify”, “check”, “confirm” |

14 | Comparison Problems | These problems involve comparing two or more entities based on certain criteria or metrics. | “compare”, “contrast”, “difference”, “similarity” |

15 | Design Problems | These problems require devising a plan, schematic, or model to fulfill certain criteria. | “design”, “plan”, “model”, “scheme” |