# Notes of Scala course on Coursera -- Week 2 (2)

Here are my notes for the week 2’s lecture of Functional Programming Principles in Scala on Coursera.

## 4. Example: Finding Fixed Points

### 4.1 Finding a fixed point of a function

A number x is called a fixed point of a function f if

For some functions f we can locate the fixed points by starting with an initial estimate and then by applying f in a repetitive way.

until the value does not vary anymore (or the change is sufficiently small).

### 4.2 Programmatic Solution

This leads to the following function for finding a fixed point:

Here is a specification of the sqrt function:
sqrt(x) = the number y such that y * y = x

Or, by dividing both sides of the equation with y :
sqrt(x) = the number y such that y = x / y

Consequently, sqrt(x) is a fixed point of the function (y => x / y) .

#### 4.3.1 First Attempt of sqrt using fixedPoint

Unfortunately, this does not converge.

Let’s add a println instruction to the function fixedPoint so we can follow the current value of guess:

sqrt(2) then produces:

#### 4.3.2 Average Damping

One way to control such oscillations is to prevent the estimation from varying too much. This is done by averaging successive values of the original sequence:

This produces

In fact, if we expand the fixed point function fixedPoint we find a similar square root function to what we developed last week.

### 4.4 Functions as Return Values

The previous examples have shown that the expressive power of a language is greatly increased if we can pass function arguments.

The following example shows that functions that return functions can also be very useful.

Consider again iteration towards a fixed point.

We begin by observing that sqrt(x) is a fixed point of the function y => x / y .

Then, the iteration converges by averaging successive values.

This technique of stabilizing by averaging is general enough to merit being abstracted into its own function.

#### Exercise:

Write a square root function using fixedPoint and averageDamp.

This expresses the elements of the algorithm as clearly as possible.

### 4.5 Summary

We saw last week that the functions are essential abstractions because they allow us to introduce general methods to perform computations as explicit and named elements in our programming language.

This week, we’ve seen that these abstractions can be combined with higher-order functions to create new abstractions. As a programmer, one must look for opportunities to abstract and reuse.

The highest level of abstraction is not always the best, but it is important to know the techniques of abstraction, so as to use them when appropriate.

## 5. Scala Syntax Summary

### 5.1 Types

A type can be:

• A numeric type: Int , Double (and Byte , Short , Char , Long , Float ),
• The Boolean type with the values true and false ,
• The String type,
• A function type, like Int => Int , (Int, Int) => Int .

Later we will see more forms of types.

### 5.2 Expressions

An expression can be:

• An identifier such as x , isGoodEnough ,
• A literal, like 0 , 1.0 , "abc" ,
• A function application, like sqrt(x) ,
• An operator application, like -x , y + x ,
• A selection, like math.abs ,
• A conditional expression, like if (x < 0) -x else x ,
• A block, like { val x = math.abs(y) ; x * 2 }
• An anonymous function, like x => x + 1 .

### 5.3 Definitions

A definition can be:

• A function definition, like def square(x: Int) = x * x
• A value definition, like val y = square(2)

A parameter can be:

• A call-by-value parameter, like (x: Int) ,
• A call-by-name parameter, like (y: => Double)