TLDR: Array programming is a way of doing computer programming where you can apply operations to a whole bunch of values at once. It's often used in scientific and engineering fields.

Array programming is a programming paradigm that allows you to work with sets of values all at once. Instead of applying operations to individual values one at a time, you can apply them to entire arrays of values simultaneously. This makes it a high-level programming model because you can think and work with large amounts of data without having to write explicit loops for each individual value.

The idea behind array programming is to find and take advantage of the similarities and relationships between elements in a set of data. Instead of breaking down the data into individual parts, like in object-oriented programming, array programming groups the data together and applies operations uniformly to the entire group.

There are several programming languages that support array programming, such as APL, J, Fortran 90, MATLAB, and R. These languages have been specifically designed to handle arrays and make it easy to perform operations on them. In these languages, you can write concise code that operates on entire arrays, which can be much more efficient and expressive than writing the same code in a scalar language like C or Pascal.

Array programming is well-suited for implicit parallelization, which means that operations can be automatically executed in parallel on multiple processors or cores. This makes it a popular choice for scientific and engineering applications that require high-performance computing.

In scalar languages like C or Pascal, operations are applied only to single values, so you have to write explicit loops to perform operations on arrays. For example, adding two arrays together in C would require writing a nested for-loop. In contrast, array-based languages like Fortran or MATLAB allow you to perform the same operation in a single line of code, making it much more concise and easier to read.

Array programming languages also provide a range of mathematical operations and functions specifically designed for working with arrays. For example, you can perform matrix multiplication, matrix inversion, and solve systems of linear equations using native functions in languages like MATLAB or GNU Octave.

Overall, array programming provides a powerful and efficient way to work with large sets of data. It allows you to express complex operations concisely and take advantage of parallel processing capabilities. Whether you're working on scientific simulations, data analysis, or engineering applications, array programming can help you write code that is both efficient and easy to understand.

Related Links:

• Programming paradigms