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[Question 1:20 points] In this question we work with polymorphic trees

type ’a tree = Empty | Node of ’a tree * ’a * ’a tree;;

Write a function mapTree that takes a function and a tree and applies the function to every node

of the tree. Here is the type

# let rec mapTree f (t: ’a tree) = ….

val mapTree : (’a -> ’b) -> ’a tree -> ’b tree =

Here is an example

# let t1 = Node(Empty, 0, (Node(Empty, 1, Empty)));;

let t2 = Node(Node(Empty,5,Empty),6,Empty);;

let t3 = Node(Node(t2,2,Node(Empty,3,Empty)),4,t1);;

# t3;;

– : int tree =

Node

(Node (Node (Node (Empty, 5, Empty), 6, Empty), 2, Node (Empty, 3, Empty)),

4, Node (Empty, 0, Node (Empty, 1, Empty)))

# mapTree (fun x -> x + 1) t3;;

– : int tree =

Node

(Node (Node (Node (Empty, 6, Empty), 7, Empty), 3, Node (Empty, 4, Empty)),

5, Node (Empty, 1, Node (Empty, 2, Empty)))

[Question 2:40 points] Suppose that f : R → R is a continuous function from the real numbers

to the real numbers. Suppose further that there is an interval [a, b] in which the function is

known to have exactly one root 1

. We will assume that2 f(a) < 0 and f(b) > 0 and the root r is

1The root of a function f is a value r such that f(r) = 0.

2When I am writing mathematical statements I do not bother to write 0.0; when I am writing code I am careful

to distinguish between 0 and 0.0.

1

somewhere in between. There is an algorithm that is reminiscent of binary search that finds (a

good approximation of) the root. The half-interval method for finding the root of a real-valued

function works as follows. The idea is to search for the root by first guessing that the root is the

midpoint of the interval. If the midpoint is the root we are done. If not, we check whether the

value of f at the midpoint is positive or negative. If it is positive then the root must be between

a and the midpoint; if it is negative, the root must be between the midpoint and b. In either case

we recursively call the search algorithm. Code this algorithm in OCaml. The following shows the

names and types that we expect.

# let rec halfint((f: float -> float),(epsilon:float))

((posValue:float),(negValue:float)) = ….

val halfint : (float -> float) * float * float * float -> float =

The parameter epsilon is used to determine the accuracy with which you are making comparisons.

Recall that you cannot reliably test floating-point numbers for equality. You may use the function

abs float as a helper. It is the floating point version of absolute value.

[Question 3:30 points] This is a classic example of a higher-order function in action. Newton’s

method can be used to generate approximate solutions to the equation f(x) = 0 where f is a

differentiable real-valued function of the real variable x. The idea can be summarized as follows:

Suppose that x0 is some point which we suspect is near a solution. We can form the linear

approximation l at x0 and solve the linear equation for a new approximate solution. Let x1 be the

solution to the linear approximation l(x) = f(x0) + f

0

(x0)(x − x0) = 0. In other words,

f(x0) + f

0

(x0)(x1 − x0) = 0

x1 − x0 = −

f(x0)

f

0(x0)

x1 = x0 −

f(x0)

f

0(x0)

If our first guess x0 was a good one, then the approximate solution x1 should be an even better

approximation to the solution of f(x) = 0. Once we have x1, we can repeat the process to obtain

x2, etc. In fact, we can repeat this process indefinitely: if, after n steps, we have an approximate

solution xn, then the next step is

xn+1 = xn −

f(xn)

f

0(xn)

.

This will produce approximate solutions to any degree of accuracy provided we have started with

a good guess x0. If we have reached a value xn such that |f(xn)| < ε, where ε is some real number
representing the tolerance, we will stop.
Implement a function called newton with the type shown below. The code outline is for your
guidance.
# let rec newton(f, (epsilon:float), (dx:float)) (guess:float) =
let close((x:float), (y:float), (epsilon:float)) = absFl(x-.y) < epsilon in
let improve((guess:float),f,(dx:float)) = .... in
if close((f guess), 0.0, epsilon)
2
then
guess
else
....
val newton : (float -> float) * float * float * float -> float =

which when given a function f, a guess x0, a tolerance ε and an interval dx, will compute a value x

0

such that |f(x

0

)| < ε. You can test this on built-in real-valued functions like sin or other functions
in the mathematics library. Please note that this method does not always work: one needs stronger
conditions on f to guarantee convergence of the approximation scheme. Never test it on tan! Here
are two examples:
let make_cubic((a:float),(b:float),(c:float)) =
fun x -> (x*.x*.x +. a *. x*.x +. b*.x +. c)

let test1 = newton(make_cubic(2.0,-3.0,1.0),0.0,0.0001,0.0001)

(* Should get -3.079599812; don’t worry if your last couple of digits are off. *)

let test2 = newton(sin,5.0,0.0001,0.0001)

(* Should get 9.42477…. *)

[Question 4:10 points] In class we say how to define a higher-order function that computes definite

integrals of the form

Z a

b

f(x)dx.

To remind you

let integral((f: float -> float),(lo:float),(hi:float),(dx:float)) =

let delta (x:float) = x +. dx in

dx *. iterSum(f,(lo +. (dx/.2.0)), hi, delta);;

where iterSum was also defined in class. In this question I want you to define a function that

computes the indefinite integral:

indIntegral(f) = Z x

0

f(y)dy.

This means that it returns a function not a number. Here is the type that I expect

# let indIntegral(f,(dx:float)) = …

val indIntegral : (float -> float) * float -> float -> float =

Use any of the functions defined in class. We will preload iterSum and integral so you won’t

have to recode them.

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