Let’s consider the Interval Scheduling Problem, in which we have a set of n requests ; the request corresponds to an interval of time starting at and finishing at , or request spans interval . Suppose that a subset of the requests is compatible if no two of them overlap in time; find the largest compatible subset of any set of requests in a reasonable number of steps.
Chapter 11. The project team problem part 1
Let’s pretend for a moment that you’re working as an undergraduate research assistant for one of the most brilliant professors at your school, which makes you more or less a glorified secretary/coffee dispenser. On the first day of class, you are tasked to divide up his CS101 class into pairs of student who will be working together in order to implement an awesome Pokemon clone. Great, yet another boring and unamusing problem to solve. However, soon you find that the problem wasn’t as easy as you originally thought it to be.
The first part of this problem solving series will deal with problems that may not seem intuitive at first, but will have extremely simple and elegant algorithms.
Tables are the most important data structure in Lua thanks to their flexibility, hence we dedicate an entire post to the use of tables. In this tutorial, you will learn how to create and manipulate tables and dictionaries and how to iterate over a table using for and while loops.
We will use the Separating Axis theorem to deduce whether two convex polygons are overlapping or not and implement the desired algorithm in both C and Lua. Collision detection is primarily used within the game development industry, but practical uses outside of this industry are also quite common.
This series more or less mirrors the series of the same name for Python. It’s in my belief that the only way to learn the in and outs of a language is to learn by practice, and by that virtue, to practice as often as possible until you get the hang of the language.
People tend to trivialize the concept of randomness in computing. While true randomness cannot be replicated by a computer, the occurrences of randomness in nature have provided us with considerable insight into the properties of a random sequence of numbers. We can replicate these properties in defined numerical sequences that behave tolerably similar to random sequences.
In this post, we will implement and examine a simple yet well known pseudorandom number generation algorithm known as the linear congruential method.