1.1 Principles of Functional Programming

Functional programming revolves around using functions as building blocks. It treats functions like values, letting you pass them around and combine them in cool ways. This approach makes code more flexible and reusable.

The key ideas are pure functions, recursion, and lazy evaluation. These concepts help create predictable, efficient code that's easier to understand and maintain. They're game-changers for solving complex problems and handling data.

Functions as First-Class Citizens

Function Fundamentals and Higher-Order Operations

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• First-class functions serve as primary building blocks in functional programming
• Functions treated as values, assigned to variables, passed as arguments, or returned from other functions
• Higher-order functions accept functions as parameters or return functions as results
• Function composition combines multiple functions to create new functions
• Enables creation of more abstract and reusable code structures

Practical Applications and Examples

• First-class functions allow for flexible code organization (

  [map](https://www.fiveableKeyTerm:map)

  ,

  [filter](https://www.fiveableKeyTerm:filter)

  ,

  [reduce](https://www.fiveableKeyTerm:reduce)

  )
• Higher-order functions facilitate powerful data transformations (sorting with custom comparators)
• Function composition creates complex operations from simpler ones (

  f(g(x))

  )
• Enhances code modularity and promotes separation of concerns
• Enables implementation of design patterns like decorators or middleware

Recursion and Referential Transparency

Recursive Problem-Solving Techniques

• Recursion involves functions calling themselves to solve problems
• Breaks complex problems into smaller, more manageable subproblems
• Base case defines termination condition for recursive calls
• Recursive case reduces problem size and makes progress towards base case
• Commonly used for tree traversals, graph algorithms, and divide-and-conquer strategies

Principles of Pure Functions and Immutability

• Referential transparency ensures function calls can be replaced with their return values
• Pure functions always produce the same output for given inputs
• Immutability prevents modification of data structures after creation
• New instances created instead of modifying existing ones
• Side-effect free functions do not modify external state or perform I/O operations
• Improves predictability, testability, and parallelization of code

Lazy Evaluation

Delayed Computation and Efficiency Gains

• Lazy evaluation defers computation of expressions until their results are needed
• Expressions evaluated only when their values are required by the program
• Can improve performance by avoiding unnecessary computations
• Allows for working with potentially infinite data structures
• Enables creation of more efficient algorithms and data processing pipelines

Implementation Strategies and Use Cases

• Thunks used to wrap expressions and delay their evaluation
• Memoization caches results of expensive computations for reuse
• Streams represent potentially infinite sequences of data
• Generator functions produce values on-demand rather than all at once
• Particularly useful in scenarios involving large datasets or complex calculations