I recently worked through Bartosz Milewski’s excellent free book “Category Theory for Programmers.” The book is available online here and here.
I had an awesome time reading the book and learning about Category Theory so I figured I’d post my solutions to the book problems online to make it easier for other people to have a similar experience. You can find my solutions below:
Section 1
p1.1
Implement, as best as you can, the identity function in your favorite language (or the second favorite, if your favorite language happens to be Haskell).
Solution
def identity(x):
return x
p1.2
Implement the composition function in your favorite language. It takes two functions as arguments and returns a function that is their composition.
Solution
def compose(f1, f2):
return lambda x: f2(f1(x))
p1.3
Write a program that tries to test that your composition function respects identity.
Solution
assert compose(lambda x: x + 4, identity)(5) == 9
assert compose(identity, lambda x: x + 4)(5) == 9
p1.4
Is the worldwide web a category in any sense? Are links morphisms?
Solution
The world wide web is indeed a category if we consider the objects to be webpages and for there to be an “arrow” between A
and B
if there is a way to get to B
from A
by clicking on links
p1.5
Is Facebook a category, with people as objects and friendships as morphisms?
Solution
No, because just because A > B
and B > C
does not imply A > C
p1.6
When is a directed graph a category?
Solution
Whenever every node has an edge that points back to it and for every two nodes A, B
such that there is a path from A
to B
, there is also an edge connecting A
to B
.
Section 2
p2.1 Define a higherorder function (or a function object) memoize
in your favorite language. This function takes a pure function f
as an argument and returns a function that behaves almost exactly like f
, except that it only calls the original function once for every argument, stores the result internally, and subsequently returns this stored result every time it’s called with the same argument. You can tell the memoized function from the original by watching its performance. For instance, try to memoize a function that takes a long time to evaluate. You’ll have to wait for the result the first time you call it, but on subsequent calls, with the sameargument, you should get the result immediately.
Solution
def memoize(f):
calls = {}
def memoized(x):
if x not in calls:
calls[x] = f(x)
return calls[x]
return memoized
p2.2 Try to memoize a function from your standard library that you normally use to produce random numbers. Does it work?
Solution This will not work
p2.3 Most random number generators can be initialized with a seed. Implement a function that takes a seed, calls the random number generator with that seed, and returns the result. Memoize that function. Does it work?
Solution
def seed_to_random(seed):
np.random.seed(seed)
return np.random.random()
memoized_random = memoize(seed_to_random)
assert np.isclose(memoized_random(0), memoized_random(0))
assert memoized_random(0) != memoized_random(1)
p2.3 Which of these C++ functions are pure? Try to memoize them and observe what happens when you call them multiple times: memoized and not.
a: The factorial function from the example in the text.
Solution
factorial
is a pure function
b: std::getchar()
Solution
getchar
is not a pure function, since it relies on the state of stdin
c:
bool f() {
std::cout << "Hello!" << std::endl;
return true;
}
Solution
f
is not a pure function, since it has the side effect of printing
d:
int f(int x) {
static int y = 0;
y += x;
return y;
}
Solution
f
is not a pure function, since it both has the side effect of incrementing y
and relies on the state of static variable y
p2.5 How many different functions are there from Bool
to Bool
? Can you implement them all?
Solution
same :: Bool > Bool
same trueorfalse = trueorfalse
opposite :: Bool > Bool
opposite trueorfalse = not trueorfalse
alwaystrue :: Bool > Bool
alwaystrue _ = True
alwaysfalse :: Bool > Bool
alwaysfalse _ = False
Section 3
p3.1 Generate a free category from:

A graph with one node and no edges Solution Add an identity arrow.

A graph with one node and one (directed) edge (hint: this edge can be composed with itself) Solution Add infinite arrows to represent every number of applications of the directed edge.

A graph with two nodes and a single arrow between them Solution Add identity arrows.

A graph with a single node and 26 arrows marked with the letters of the alphabet: a, b, c … z. Solution Add an identity arrow, and then add infinite arrows, one for every combination of az of any length.
p3.2 What kind of order is this?
 A set of sets with the inclusion relation:
A
is included inB
if every element ofA
is also an element ofB
. Solution This is a partial order. For any
(a, b)
there is at most onea > b
and ifa > b
andb > a
thena
andb
have the same elements and are the same set.  Since there might be some
(a, b)
wherea intersect b
is empty, this is not a total order
 For any
 C++ types with the following subtyping relation:
T1
is a subtype ofT2
if a pointer toT1
can be passed to a function that expects a pointer toT2
without triggering a compilation error. Solution This is a partial order. For any
(t1, t2)
there is at most onet1 > t2
, and ift1 > t2
andt2 > t1
thent1
andt2
are the same type.  There are types not connected by a subtype relation, so this is not a total order.
 For any
p3.3 Considering that Bool
is a set of two values True
and False
, show that it forms two (settheoretical) monoids with respect to, respectively,operator AND
and OR
.
Solution
AND
* Closure: The output of AND
is boolean
* Identity: The identity is True
* Associative: Easy to show by enumeration
OR
* Closure: The output of OR
is boolean
* Identity: The identity is False
* Associative: Easy to show by enumeration
p3.4 Represent the Bool
monoid with the AND
operator as a category: List the morphisms and their rules of composition.
Solution
The single element in this category is the Bool
type. The morphisms are AND True
(identity) and AND False
. The composition of these two is AND False
.
p3.5 Represent addition modulo 3 as a monoid category.
Solution
The single element in this category is the the type [Int < 3, >= 0]
.
The morphisms are
* A: add 3n
(identity)
* B: add 1 + 3n
* C: add 2 + 3n
The morphisms in this category are closed under association because B . B
is C
and both B . C
, C . B
are A
Section 4
p4.1 Construct the Kleisli category for partial functions (define composition and identity).
Solution
class Optional:
def __init__(self, value):
self._value = value
def is_valid(self):
return self._value is not None
def get(self):
assert self.is_valid()
return self._value
def compose(f1, f2):
def composed(x):
f1out = f1(x)
return f2(f1out.get()) if f1out.is_valid() else Optional(None)
return composed
def identity(x):
return Optional(x)
p4.2 Implement the embellished function safe_reciprocal
that returns a valid reciprocal of its argument, if it’s different from zero.
Solution
def safe_root(x):
return Optional(np.sqrt(x)) if x >= 0 else Optional(None)
def safe_reciprocal(x):
return Optional(1 / float(x)) if x != 0 else Optional(None)
assert not safe_root(1).is_valid()
assert np.isclose(safe_root(4).get(), 2.0)
assert not safe_reciprocal(0).is_valid()
assert np.isclose(safe_reciprocal(4).get(), 0.25)
p4.3 Compose safe_root
and safe_reciprocal
to implement safe_root_reciprocal
that calculates sqrt(1/x)
whenever possible.
Solution
safe_root_reciprocal = compose(safe_reciprocal, safe_root)
assert not safe_root_reciprocal(0).is_valid()
assert not safe_root_reciprocal(5).is_valid()
assert np.isclose(safe_root_reciprocal(0.25).get(), 2)
Section 5
p5.1 Show that the terminal object is unique up to unique isomorphism.
Solution
Consider two terminal objects A, B
. There is exactly one morphism m1
from A > B
since B
is terminal and exactly one morphism m2
from B > A
since A
is terminal. Then m1 . m2
is a morphism from B > B
and m2 . m1
is a morphism from A > A
. Since B
is terminal, there is exactly one morphism from B > B
, so m1 . m2
is the identity.
Therefore m1, m2
form an isomorphism between A
and B
. Since there are no other morphisms between A
and B
, m1, m2
is a unique isomorphism.
p5.2 What is a product of two objects in a poset? Hint: Use the universal construction.
Solution
The product of two objects A, B
in a poset is the object C
that is less than both A
and B
(i.e. exists: p: C > A
and q: C > B
) and for any other object D
that is also less than A
and B
, exists D > C
. This object does not always exist.
Sufficiency
Say we have such an A, B, C
. Now consider some object D
such that p2: D > A
, q2: D > B
. Then we have some m: D > C
so p1 . m: D > A
and q1 . m: D > B
. Now since there is at most one morphism between any pair of objects in a poset, it’s true that p2 = p1 . m
and q2 = q1 . m
, so m
factorizes p
and q
.
Necessity
If there were some object D
such that D > A
, D > B
but not D > C
, then there is no morphism m
such that p1 = p2 . m
since m
must be a morphism from D > C
. Therefore C
is not the product of A,B
.
p5.3 What is a coproduct of two objects in a poset?
Solution
We just reverse the arrows in p5.2. The coproduct of two objects A, B
in a poset is the object C
that is greater than both A
and B
(i.e. exists: p: A > C
and q: B > C
) and for any other object D
that is also greater than A
and B
, exists C > D
. This object does not always exist.
p5.4 Implement the equivalent of Haskell Either
as a generic type in your favorite language (other than Haskell).
Solution
class EitherAbstract(object):
pass
class RightEither(EitherAbstract):
def __init__(self, right):
self.right = right
class LeftEither(EitherAbstract):
def __init__(self, left):
self.left = left
# doing something like this in python is a recipe for disaster :)
def either_factory(left_type, right_type):
def generate(left=None, right=None):
assert ((left is None) ^ (right is None))
if left is not None:
assert isinstance(left, left_type)
out = LeftEither(left=left)
if right is not None:
assert isinstance(right, right_type)
out = RightEither(right=right)
return out
return generate
my_left = either_factory(int, str)(left=5)
assert my_left.left == 5
my_right = either_factory(int, str)(right="5")
assert(my_right.right == "5")
try:
either_factory(int, str)()
print("failed")
except AssertionError:
pass
try:
either_factory(int, str)(left=5, right="5")
print("failed")
except AssertionError:
pass
p5.5 Show that Either
is a “better” coproduct than int
equipped with two injections:
int i(int n) { return n; }
int j(bool b) { return b? 0: 1; }
Hint: Define a function int m(Either const & e);
that factorizes i
and j
.
Solution
# Consider the following injections from int and bool into int
def int_to_int(int_param):
assert isinstance(int_param, int)
return int_param
def bool_to_int(bool_param):
assert isinstance(bool_param, bool)
return 1 if bool_param else 0
# We can define the following morphism from Either into int
def either_to_int(either_param):
isinstance(either_param, EitherAbstract)
if either_param.kind == "left":
out = int_to_int(either_param.left)
elif either_param.kind == "right":
out = bool_to_int(either_param.right)
return out
"""
Then
bool_to_int(x) = either_to_int (either_factory(int, bool)(left=x))
int_to_int(y) = either_to_int (either_factory(int, bool)(right=y))
so either_to_int factorizes bool_to_int and int_to_int
"""
assert either_to_int(either_factory(int, bool)(left=5)) == int_to_int(5)
assert either_to_int(either_factory(int, bool)(right=True)) == bool_to_int(True)
assert either_to_int(either_factory(int, bool)(right=False)) == bool_to_int(False)
p5.6 Continuing the previous problem: How would you argue that int
with the two injections i
and j
cannot be “better” than Either
?
Solution
Say there exists some function impossible_m
such that
either_factory(int, bool)(left=x) = impossible_m ( int_to_int (x))
either_factory(int, bool)(right=y) = impossible_m ( bool_to_int (y))
For all int x
and bool y
. Then it must be the case that:
impossible_m(1) = LeftEither(left=1)
impossible_m(1) = RightEither(right=1)
Which is not possible, because the output of a function for a given input argument must be unique.
p5.7 Still continuing: What about these injections?
int i(int n) {
if (n < 0) return n;
return n + 2;
}
int j(bool b) { return b? 0: 1; }
Solution
Say there exists some function new_m
such that
def int_to_int_2(int_param):
assert isinstance(int_param, int)
return int_param if int_param < 0 else int_param + 2
either_factory(int, bool)(left=x) = new_m ( int_to_int_2 (x))
Then it must be the case that
new_m(x) = LeftEither(x) for all x
new_m(2) = LeftEither(0)
new_m(3) = LeftEither(1)
new_m(4) = LeftEither(2)
...
new_m(max_int) = Left(max_int  2)
Since there are only a finite number of integers in python/C++, Left(max_int  1)
and Left(max_int)
cannot be in the domain of new_m
.
p5.8 Come up with an inferior candidate for a coproduct of int
and bool
that cannot be better than Either
because it allows multiple acceptable morphisms from it to Either
Solution
Consider some type SuperEither
defined as
data SuperEither = IntBoolTuple (Int, Int)  BoolBoolTuple (Bool, Bool)
We can define injections into this type from int
and bool
of the forms
intint :: Int > SuperEither;
intint x = IntIntTuple (x, x)
boolbool :: Bool > SuperEither;
boolbool x = BoolBoolTuple (x, x)
Then we can define multiple morphisms from SuperEither
into Either
that take either the first or second element.
Section 6
p6.1 Show the isomorphism between Maybe a
and Either () a
.
Solution We can define the following two functions, which serve as inverses
maybeToEither :: Maybe a > Either () a
maybeToEither inputMaybe =
case inputMaybe of
Just a > Right a
Nothing > Left ()
eitherToMaybe :: Either () a > Maybe a
eitherToMaybe inputEither =
case inputEither of
Right a > Just a
Left () > Nothing
p6.2 Here’s a sum type defined in Haskell:
data Shape = Circle Float
 Rect Float Float
When we want to define a function like area that acts on a Shape
, we do it by pattern matching on the two constructors:
area :: Shape > Float
area (Circle r) = pi * r * r
area (Rect d h) = d * h
Implement Shape
in C++ or Java as an interface and create two classes: Circle
and Rect
. Implement area as a virtual function.
Solution
# I'll use python again, just for fun
class AbstractShape(object):
def area(self):
assert NotImplementedError()
def circ(self):
assert NotImplementedError()
class Circle(AbstractShape):
def __init__(self, radius):
self.radius = radius
def area(self):
return self.radius**2 * np.pi
def circ(self):
return 2 * self.radius * np.pi
class Rect(AbstractShape):
def __init__(self, height, width):
self.height = height
self.width = width
def area(self):
return self.height * self.width
def circ(self):
return 2 * self.height + 2 * self.width
rect = Rect(3, 5)
assert rect.circ() == 16
assert rect.area() == 15
p6.3 Continuing with the previous example: We can easily add a new function circ
that calculates the circumference of a Shape
. We can do it without touching the definition of Shape
:
circ :: Shape > Float
circ (Circle r) = 2.0 * pi * r
circ (Rect d h) = 2.0 * (d + h)
Add circ
to your C++ or Java implementation. What parts of the original code did you have to touch?
Solution
See above. We needed to add it to each class, including AbstractShape
.
p6.4 Continuing further: Add a new shape, Square
, to Shape
and make all the necessary updates. What code did you have to touch in Haskell vs. C++ or Java? (Even if you’re not a Haskell programmer, the modifications should be pretty obvious.)
Solution
For haskell we need to update the Shape
definition and add another line to circ
and area
implementations. For python we needed to write a new class with a new initializer, inheriting from Rect
Haskell:
data Shape = Circle Float  Rect Float Float  Square Float
area :: Shape > Float
area (Circle r) = pi * r * r
area (Rect d h) = d * h
area (Square h) = h * h
circ :: Shape > Float
circ (Circle r) = 2.0 * pi * r
circ (Rect d h) = 2.0 * (d + h)
circ (Square h) = 4.0 * h
Python:
class Square(Rect):
def __init__(self, length):
self.height = length
self.width = length
square = Square(5)
assert square.circ() == 20
assert square.area() == 25
p6.5 Show that a + a = 2 * a
holds for types (up to isomorphism). Remember that 2
corresponds to Bool
, according to our translation table.
Solution
a + a
is equivalent to Either a a
and 2 * a
is equivalent to (Bool, a)
. We can define the following invertible functions between them.
aPlusAToTwoTimesA :: Either a a > (Bool, a)
aPlusAToTwoTimesA eitherAA =
case eitherAA of
Left a > (True, a)
Right a > (False, a)
twoTimesAToAPlusA :: (Bool, a) > Either a a
twoTimesAToAPlusA twoTimesA =
case twoTimesA of
(True, a) > Left a
(False, a) > Right a
Section 7: Functors
p7.1 Can we turn the Maybe
type constructor into a functor by defining: fmap _ _ = Nothing
which ignores both of its arguments? (Hint: Check the functor laws.)
Solution
No, this mapping of morphisms does not preserve the identity. For some Just a
, we see that:
(fmap id) Just a = Nothing
id Just a = Just a
p7.2 Prove functor laws for the reader
functor. Hint: it’s really simple.
Solution We need to use equational reasoning to prove that fmap maintains identity and preserves composition
Identity
fmap id (a>b) =
(.) id (a>b) =
id (a>b)
Composition
fmap ((c>d) . (b>c)) (a>b) =
(c>d) . (b>c) . (a>b) =
(c>d) . fmap ((b>c) (a>b)) =
fmap (c>d) (fmap (b>c) (a>b))
p7.3 Implement the reader
functor in your second favorite language (the first being Haskell, of course).
Solution
def reader_functor_fmap(f, r_to_a):
return lambda r: f(r_to_a(r))
def r_to_0(r):
return 0
def r_to_1(r):
return 1
r_to_5 = reader_functor_fmap(lambda x: x + 5, r_to_0)
assert r_to_0("r") == 0
assert r_to_1("r") == 1
assert r_to_5("r") == 5
p7.4 Prove the functor laws for the list
functor. Assume that the laws are true for the tail part of the list
you’re applying it to (in other words, use induction).
Solution
Base Case
 Identity
fmap id Nil = Nil = id Nil
 Composition
fmap (f . g) Nil = Nil = fmap f (Nil) = fmap f (fmap g Nil)
Inductive Step
 Identity
fmap id (Cons x t) = Cons (id x) (fmap id t)) = Cons (id x) (id t)) = Cons (x t) = id Cons (x t)
 Composition
fmap (f . g) (Cons x t) = Cons ((f . g) x) (fmap (f . g) t)) // definition of fmap = Cons ((f . g) x) ((fmap f . fmap g) t) // induction = fmap f (Cons (g x) (fmap g t)) // definition of fmap = fmap f (fmap g (Cons (x t))) // definition of fmap = (fmap f . fmap g) (Cons (x t))
Section 8: Functorality
p8.1 Show that the data type: data Pair a b = Pair a b
is a bifunctor. For additional credit implement all three methods of Bifunctor
and use equational reasoning to show that these definitions are compatible with the default implementations whenever they can be applied.
Solution Say we keep one of the arguments constant, then the fmap for both sides is just:
fmap f Pair (a), (C) = Pair (f a) (C)
Identity
fmap id Pair a C = Pair id a C = Pair a C
Composition
fmap f*g Pair a C = Pair f*g(a) C = fmap f Pair g(a) C = fmap f fmap g Pair a c
The three methods of Bifunctor
data Pair a b = Pair a b
pairBimap :: (a > c) > (b > d) > Pair a b > Pair c d
pairBimap g h (Pair a b) = Pair (g a) (h b)
pairFirst :: (a > c) > Pair a b > Pair c b
pairFirst g (Pair a b) = Pair (g a) b
pairSecond :: (b > d) > Pair a b > Pair a d
pairSecond f (Pair a b) = Pair a (f b)
Proof that these definitions are compatible with the default implementations whenever they can be applied.
Proof of pairBimap
(pairBimap g h) (Pair a b) =
Pair (g a) (h b) // definition of pairBimap
pairFirst g (Pair a (h b)) // definition of pairFirst
(pairFirst g . pairSecond h) (Pair a b) // definition of pairSecond
Which means that
pairBimap g h = pairFirst g . pairSecond h
Proof of pairFirst
pairFirst g (Pair a b) =
Pair (g a) b // definition of pairFirst
Pair (g a) (id b) // definition of id
pairBimap (g id) Pair a b // definition of pairBimap
Which means that
pairFirst g = pairBimap g id
Proof of pairSecond
pairSecond f (Pair a b) =
Pair a (f b) // definition of pairSecond
Pair (id a) (f b) // definition of id
pairBimap (id f) Pair a b // definition of pairBimap
Which means that
pairSecond = pairBimap id
p8.2 Show the isomorphism between the standard definition of Maybe
and this desugaring: type Maybe' a = Either (Const () a) (Identity a)
Hint: Define two mappings between the two implementations. For additional credit, show that they are the inverse of each other using equational reasoning.
Solution
data MyIdentity a = MyIdentity a
data MyConst c a = MyConst c
type Maybe' a = Either (MyConst () a) (MyIdentity a)
desugaredToMaybe :: Maybe' a > Maybe a
desugaredToMaybe (Left (MyConst ())) = Nothing
desugaredToMaybe (Right (MyIdentity a)) = Just a
maybeToDesugared :: Maybe a > Maybe' a
maybeToDesugared Nothing = Left (MyConst ())
maybeToDesugared (Just a) = Right (MyIdentity a)
We show that they are the inverse of each other using equational reasoning
maybeToDesugared desugaredToMaybe (Left (MyConst ()))
= maybeToDesugared Nothing
= maybeToDesugared Left (MyConst ())
desugaredToMaybe maybeToDesugared Nothing
= desugaredToMaybe (Left (MyConst ()))
= Nothing
maybeToDesugared desugaredToMaybe (Right (MyIdentity a))
= maybeToDesugared Just a
= Right (MyIdentity a)
desugaredToMaybe maybeToDesugared Just a
= desugaredToMaybe Right (MyIdentity a)
= Just a
p8.3 Let’s try another data structure. I call it a PreList
because it’s a precursor to a List. It replaces recursion with a type parameter b
: data PreList a b = Nil  Cons a b
. You could recover our earlier definition of a List
by recursively applying PreList
to itself (we’ll see how it’s done when we talk about fixed points). Show that PreList
is an instance of Bifunctor
.
Solution Lets form the following mapping
fmapFull:: (a > c) > (b > d) > (PreList a b) > (PreList c d)
fmapFull f g Nil = Nil
fmapFull f g Cons a b = Cons (f a) (g b)
Say we keep b
constant (WLOG). Then the fmap
for a
is
fmap f Nil = Nil
fmap f Cons a C = Cons (f a) C
This is a functor because Identity
fmap id Nil = Nil
fmap id Cons a C = Cons id a C = Cons a C
Composition
fmap f . g Nil = Nil
fmap f . g Cons a C =
Cons f . g a C =
fmap f Cons g a C =
fmap f (fmap g Cons a C)
p8.4 Show that the following data types define bifunctors in a
and b
:
data K2 c a b = K2 c
data Fst a b = Fst a
data Snd a b = Snd b
For additional credit, check your solutions agains Conor McBride’s paper Clowns to the Left of me, Jokers to the Right.
Solution
K2
:
Without loss of generality, if we hold b
constant, then K2
becomes Const
, which is a functor
Fst
:
If we hold a
constant, then Fst
becomes Const
, which is a functor
If we hold b
constant, then Fst
becomes Identity
, which is a functor
Snd
:
If we hold a
constant, then Snd
becomes Identity
, which is a functor
If we hold b
constant, then Snd
becomes Const
, which is a functor
p8.5 Define a bifunctor in a language other than Haskell. Implement bimap
for a generic pair in that language.
Solution
class Bifunctor(object):
def apply_bimap(self, f, g):
assert False
@classmethod
def first(cls, f):
return lambda pair: pair.apply_bimap(f, lambda x: x)
@classmethod
def second(cls, g):
return lambda pair: pair.apply_bimap(lambda x: x, g)
@classmethod
def bimap(cls, f, g):
return lambda pair: pair.apply_bimap(f, g)
class Pair(Bifunctor):
def __init__(self, aval, bval):
self.aval = aval
self.bval = bval
def apply_bimap(self, f, g):
return Pair(f(self.aval), g(self.bval))
p = Pair(5, "4")
first_mapped = Bifunctor.first(lambda x: x + 1)(p)
assert first_mapped.aval == 6
assert first_mapped.bval == "4"
second_mapped = Bifunctor.second(lambda x: x + "1")(p)
assert second_mapped.aval == 5
assert second_mapped.bval == "41"
bimapped = Bifunctor.bimap(lambda x: x + 1, lambda s: s + "1")(p)
assert bimapped.aval
p8.6 Should std::map
be considered a bifunctor or a profunctor in the two template arguments Key
and T
? How would you redesign this data type to make it so?
Solution
std::map
should be considered a profunctor in Key
and T
.
We can define it as a Profunctor as follows:
get :: a > Maybe b
instance Profunctor get where
dimap f g get = lmap f . rmap g
lmap f get = \x > get (f x)
rmap g get = \x > fmap g (get x)
Section 9: Function Types (No Challenges)
Section 10: Natural Transformations
p10.1 Define a natural transformation from the Maybe
functor to the list
functor. Prove the naturality condition for it.
Solution
natTrans:: Maybe a > [a]
natTrans (Just x) = [x]
natTrans Nothing = []
The naturality condition is G f ◦ αa = αb ◦ F f
, which translates to fmap_list f . natTrans = natTrans . fmap_maybe f
Nothing
Case:
fmap_list f . natTrans Nothing =
fmap_list f [] =
[] =
natTrans Nothing =
natTrans . fmap_maybe f Nothing
(Just x)
Case:
fmap_list f . natTrans (Just x) =
fmap_list f [x] =
[f(x)] =
natTrans (Just (f x)) =
natTrans . fmap_maybe f (Just x)
p10.2 Define at least two different natural transformations between Reader ()
and the list
functor. How many different lists of ()
are there?
Solution
natTransRL1:: (() > a) > [a]
natTransRL1 _ = []
natTransRL2:: (() > a) > [a]
natTransRL2 g = [g ()]
natTransRL3:: (() > a) > [a]
natTransRL3 g = fmap g [(), ()]
Since there are an infinite number of lists of [(), ...]
, there are an infinite number of these natural transformations.
p10.3 Continue the previous exercise with Reader Bool
and Maybe
.
Solution
There are three natural transformations from Reader Bool > Maybe
natTransRB1:: (Bool > a) > Maybe a
natTransRB1 _ = Nothing
natTransRB2:: (Bool > a) > Maybe a
natTransRB2 g = Just (g True)
natTransRB3:: (Bool > a) > Maybe a
natTransRB3 g = Just (g False)
p10.4 Show that horizontal composition of natural transformation satisfies the naturality condition (hint: use components). It’s a good exercise in diagram chasing.
Solution
We have the functors F, G
and the natural transformations:
αa:: F a > F'a
βa:: G a > G'a
We need to show that (G' . F') f . (β ◦ α)a = (β ◦ α)b . (G . F) f
(β ◦ α)b . (G . F) f =
(βF'b . Gαb) . G . F f = // definition of horizontal composition
βF'b . G . F' f . αa = // G αb :: G (F b) > G (F'b)
(G' . F') f . (β ◦ α)a = // βF'b :: G (F'a) > G'(F'a)
p10.5 Write a short essay about how you may enjoy writing down the evident diagrams needed to prove the interchange law.
Solution
If it’s the case that:
F β'> F'
F' α'> F''
G β> G'
G' α> G''
Then by the definition of horizontal composition it’s simple to see that:
FG (β' ◦ β)> F'G' (α' ◦ α)> F''G''
FG (β' . α') ◦ (β . α)> F''G''
Also, by horizontal composition:
FG (β' ◦ β)> F'G'
F'G' (α' ◦ α)> F''G''
FG (β' ◦ β) . (α' ◦ α)> F''G''
so (β' ◦ β) . (α' ◦ α)
and (β' . α') ◦ (β . α)
have the equivalent effect on FG
p10.6 Create a few test cases for the opposite naturality condition of transformations between different Op
functors. Here’s one choice:
op :: Op Bool Int
op = Op (\x > x > 0)
and
f :: String > Int
f x = read x
Solution
newtype Op r a = Op (a > r)
contramap f (Op g) = Op (g . f)
unwrap_op :: Op a b > b > a
unwrap_op (Op f) x = f x
 test 1
op1 :: Op Bool Int
op1 = Op (\x > (x > 0))
f1 :: Bool > Int
f1 x = if x then 1 else 0
opBoolToOpChar :: Op Bool a > Op Char a
opBoolToOpChar (Op aToBool) = Op (\x > if aToBool x then 'a' else 'b')
boolchar_contra_f_op1 :: Op Char Bool
boolchar_contra_f_op1 = opBoolToOpChar ((contramap f1) op1)
contra_f_boolchar_op1 :: Op Char Bool
contra_f_boolchar_op1 = contramap f1 (opBoolToOpChar op1)
test1a = (unwrap_op boolchar_contra_f_op1 True) == (unwrap_op contra_f_boolchar_op1 True)
test1b = (unwrap_op boolchar_contra_f_op1 False) == (unwrap_op contra_f_boolchar_op1 False)
 test 2
op2 :: Op String Double
op2 = Op (\x > show x)
f2 :: Int > Double
f2 x = sqrt (fromIntegral x)
opStringToOpInt :: Op String a > Op Int a
opStringToOpInt (Op aToString) = Op (\x > length (aToString x))
stringint_contra_f_op2 :: Op Int Int
stringint_contra_f_op2 = opStringToOpInt ((contramap f2) op2)
contra_f_stringint_op2 :: Op Int Int
contra_f_stringint_op2 = contramap f2 (opStringToOpInt op2)
test2a = (unwrap_op stringint_contra_f_op2 5) == (unwrap_op contra_f_stringint_op2 5)
test2b = (unwrap_op stringint_contra_f_op2 2) == (unwrap_op contra_f_stringint_op2 2)
Section 11: Declarative Programming (No Challenges)
Section 12: Limits and Colimits
p12.1 How would you describe a pushout in the category of C++ classes?
Solution
We are working in the C++ types category with subclasses as morphisms. For the span 1 < 2 > 3
we have a class 2 that inherits from 1 and 3, and since we are working with colimits, the apex is some 4 such that 1 > 4 < 3
. The pushout is the colimit of this diagram, which is the universal 4 such that the colimit is also the subclass of every other candidate. This is the class 4 that has the maximum amount of shared functionality such that it can still be a superclass of 1 and 3.
p12.2 Show that the limit of the identity functor Id :: C > C
is the initial object.
Solution The identity functor forms diagrams consisting of every item in C. The apex of each diagram must have morphisms to every other item, and the limit object must have unique morphisms to every other limit candidate, which is every other item. Therefore the limit must be the initial object.
p12.3 Subsets of a given set form a category. A morphism in that category is defined to be an arrow connecting two sets if the first is the subset of the second. What is a pullback of two sets in such a category? What’s a pushout? What are the initial and terminal objects?
Solution The pushout is the intersection of the two sets (the largest set contained in them both) and the pullback is the union of those sets (the smallest set that contains them both). The initial object is the empty set and the terminal object is the “given set” that contains all of the elements and that all of the other elements are subsets of.
p12.4 Can you guess what a coequalizer is?
Solution
The coequalizer is the equalizer in the opposite category. Given some 2 morphisms f: b > a
and g: b > a
, the coequalizer is the colimit object c
and associated morphism p: a > c
such that p . f = p . g
. That is, for any other c'
with morphism p'
there exists some u
such that p' = p . u
.
Over Set, the coequalizer defines a transformation of f and g’s codomains that makes them equal to each other.
p12.5 Show that, in a category with a terminal object, a pullback towards the terminal object is a product.
Solution
Consider a diagram formed by the three object category I
of the form 1 f> t <g 2
such that t is the terminal object. Since f and g are unique, the category of such diagrams is isomorphic to the category of diagrams formed by the two object discrete category consisting of only 1,2
without morphisms. The limit of this category is the product, so the pullback towards the terminal object is the product.
p12.6 Similarly, show that a pushout from an initial object (if one exists) is the coproduct.
Solution
Consider a diagram formed by the three object category I of the form 1 <f i g> 2
such that i
is the initial object. Since f
and g
are unique, the category of such diagrams is isomorphic to the category of diagrams formed by the two object discrete category consisting of only 1,2
without morphisms. The colimit of this category is the coproduct, so the pushout towards the initial object is the coproduct.
Section 13: Free Monoids
p13.1 You might think (as I did, originally) that the requirement that a homomorphism of monoids preserve the unit is redundant. After all, we know that for all a
, h a * h e = h (a * e) = h a
So h e
acts like a right unit (and, by analogy, as a left unit). The problem is that h a
, for all a
might only cover a submonoid of the target monoid. There may be a “true” unit outside of the image of h. Show that an isomorphism between monoids that preserves multiplication must automatically preserve unit.
Solution
Say f: A > A'
is a monoid isomorphishm. Then there exists some g: A' > A
such that g f a = a
. Given the unit u
in A
, for all a'
in A'
, we see g (a' * f u) = g a' * g f u = g a' * u = g a'
. Since g
is injective, this means that a' = a' * f u
, so f u
is the right unit for all a' in A'
. We can do the same to show f u
is the left unit as well.
p13.2 Consider a monoid homomorphism from lists of integers with concatenation to integers with multiplication. What is the image of the empty list []
? Assume that all singleton lists are mapped to the integers they contain, that is [3]
is mapped to 3
, etc. What’s the image of [1, 2, 3, 4]
? How many different lists map to the integer 12? Is there any other homomorphism between the two monoids?
Solution
The image of the empty list is 1
, and the image of [1,2,3,4]
is 1*2*3*4=24
. The lists [12,1] [1,12] [6,2] [2,6] [3,4] [4,3] [2,2,3] [2,3,2] [3,2,2]
all map to 12
.
The function that maps all lists of integers to 1
is also a homomorphism, because the unit [1]
is preserved and for any two lists l1,l2
we see that h (l1 ++ l2) = 1 = 1 * 1 = h l1 * h l2
p13.3 What is the free monoid generated by a oneelement set? Can you see what it’s isomorphic to?
Solution
This monoid is lists of unit ()
with concatenation. This is isomorphic to integers over addition.
forward :: List () > Int
forward x = length x
inverse :: Int > List ()
inverse x = replicate x [()]
Section 14: Representable Functors
p14.1 Show that the homfunctors map identity morphisms in C
to corresponding identity functions in Set
.
Solution
When we apply the functor C(a, )
to some function f
, we get a function that performs the action C (a, f) h = f . h
on any morphism h: a > x
in the homset Hom(a, x)
. If f: x > x
is the identity morphism, f . h = h
, so C (a, f) h = h
, and C (a, f)
is the identity morphism as well.
p14.2 Show that Maybe
is not representable.
Solution
If Maybe were representable, then we would be able to implement a function of the for beta :: Maybe x > (a > x)
. However, it is not possible to implement a function that accepts None
and return a > x
for any arbitrary x
type.
p14.3 Is the Reader
functor representable?
Solution
Yes, the Reader
functor is the homfunctor over haskell types and it is isomorphic to itself.
p14.4 Using Stream
representation, memoize a function that squares its argument.
Solution
data Stream x = Cons x (Stream x)
instance Representable Stream where
type Rep Stream = Int
tabulate f = Cons (f 0) (tabulate (f . (+1)))
index (Cons b bs) n = if n == 0 then b else index bs (n  1)
squareArg :: Int > Int
squareArg x = x * x
memoizedSquares :: Stream Int
memoizedSquares = tabulate squareArg
zerothSquare :: Int
zerothSquare = index memoizedSquares 0
zerothSquareTrue = zerothSquare == 0
thirdSquare :: Int
thirdSquare = index memoizedSquares 3
thirdSquareTrue = thirdSquare == 9
fifthSquare :: Int
fifthSquare = index memoizedSquares 5
fifthSquareTrue = fifthSquare == 25
p14.5 Show that tabulate
and index
for Stream
are indeed the inverse of each other. (Hint: use induction.)
Solution
We want to prove that for all n
, index tabulate f n = f n
Base Case
index (tabulate f) 0 = // definition of tabulate
index (Cons (f 0) (tabulate (f . (+1)))) 0 = // definition of index
f 0
Inductive Step
index (tabulate f) n = // definition of tabulate
index (Cons (f 0) (tabulate (f . (+1)))) n = // definition of index
index (tabulate (f . (+1))) (n  1) = // inductive assumption
f . (+1) . (n  1) =
f n
Section 15: The Yoneda Lemma
p15.1 Show that the two functions phi
and psi
that form the Yoneda isomorphism in Haskell are inverses of each other.
phi :: (forall x . (a > x) > F x) > F a
phi alpha = alpha id
psi :: F a > (forall x . (a > x) > F x)
psi fa h = fmap h fa
Solution
Note psi
can be written as psi fa = \h > fmap h fa
Forward
(phi . psi) fa =
phi (\h > fmap h fa) =
(\h > fmap h fa) id =
fmap id fa =
fa
Backward
(psi . phi) alpha =
psi (alpha id) =
\h > fmap h (alpha id) =
\h > (alpha h id) =
\h > alpha h =
alpha
p15.2 A discrete category is one that has objects but no morphisms other than identity morphisms. How does the Yoneda lemma work for functors from such a category?
Solution
Any homfunctor C(a, )
from the discrete category maps a
to the singleton set and all other objects to the empty set. For any functor F
from the discrete category to Set
, there are N
morphisms (the itemselection morphisms) between the singleton set and F a
, where N
is the number of elements of F a
. Since there is one morphism from the empty set to each other set, each of those N morphisms from singleton to F a
indicate a unique natural transformation from C(a, )
to F
, so there is onetoone correspondence between these natural transformations and elements of F a
.
p15.3 A list of units [()]
contains no other information but its length. So, as a data type, it can be considered an encoding of integers. An empty list encodes zero, a singleton [()]
(a value, not a type) encodes one, and so on. Construct another representation of this data type using the Yoneda lemma for the list
functor.
Solution
By the Yoneda lemma, the natural transformations from C(a, )
(in this case () > x
) to F
(in this case List x
) are onetoone with the elements of F a
. So the data type D (() > x) > List x
is another representation of List ()
.
It’s pretty easy to see why this is the case  a function of the form f: () > x
is essentially a container for a single value of x
. So the elements of D
are all of the form:
d1 f = [f ()]
d2 f = [f (), f ()]
...
Section 16: Yoneda Embedding
p16.1 Express the coYoneda embedding in Haskell.
Solution
forward :: (a > b) > ((x > a) > (x > b))
forward atob = \f > atob . f
backward :: ((x > a) > (x > b)) > (a > b)
backward xToAToXToB = \a > (xToAToXToB id) a
p16.3 Work out the Yoneda embedding for a monoid. What functor corresponds to the monoid’s single object? What natural transformations correspond to monoid morphisms?
Solution
In the singleelement category view of monoid, we have a single element and the morphisms follow the monoid association rules. We will call this category M
.
The Yoneda embedding maps the single object a
to the functor M(a, )
, which is the functor in [M, Set]
that maps the single element a
to the set M(a,a)
.
The Yoneda embedding maps each morphism in M
to the identity natural transformation that maps the functor M(a, )
to itself.
p16.4 What is the application of the covariant Yoneda embedding to preorders? (Question suggested by Gershom Bazerman.)
In a preorder category C
, if and only if a morphism f: b > a
exists, we have exactly one natural transformation between C(a, )
and C(b, )
. Since there are no functions that map nonempty sets into the empty set, we see that if C(a, x)
is nonempty, then C(b, x)
must be nonempty as well.
Therefore, we have the condition: b <= a
, if and only if for all x
, a <= x
implies b <= x
p16.5 Yoneda embedding can be used to embed an arbitrary functor category [C, D]
in the functor category [[C, D], Set]
. Figure out how it works on morphisms (which in this case are natural transformations).
Solution
For any natural transformation NatAB
between the functors Fb: C > D
and Fa: C > D
such that NatAB: Fb > Fa
, the Yoneda embedding maps it to the natural transformation:
NYoneda: [C, D](Fa, ) > [C, D](Fb, )
Where
[C, D](Fa, ): Fx > NatAX
NatAX: Fa > Fx
[C, D](Fb, ): Fx > NatBX
NatBX: Fb > Fx
NYoneda
operates on [C, D](Fa, )
by postcomposing NatAB
to the natural transformations in the output NatAX
, which maps the output set to NatBX
, which maps ([C, D](Fa, ): Fx > NatAX) > ([C, D](Fb, ): Fx > NatBX)
Section 17: It’s All About Morphisms
p17.1 Consider some degenerate cases of a naturality condition and draw the appropriate diagrams. For instance, what happens if either functor F
or G
map both objects a
and b
(the ends of f :: a > b
) to the same object, e.g., F a = F b
or G a = G b
? (Notice that you get a cone or a cocone this way.) Then consider cases where either F a = G a
or F b = G b
. Finally, what if you start with a morphism that loops on itself — f :: a > a
?
Solution
For the following subproblems, let’s assume we have some function f: a>b
and natural transformation α
between functors F
and G
.
p17.1.1
Say Fa = Fb
. Then Ga = Gb
because:
(α . Ff) Fa =
α . Fb =
α . Fa =
Ga
(Gf * α) Fa =
Gf Ga =
Gb
p17.1.2
Say Ga = Gb
. Then αB Fb = αA Fa
, but we can’t conclude that Fb = Fa
, because it’s possible that G
is the constant functor.
p17.1.3
Say Fa = Ga
. Then Gf: Fa > Gb
and since αA Fa = Ga
, αA
is the identity.
p17.1.4
Say Fb = Gb
. Then Ff: Fa > Gb
and since αB Fb = Gb
, αB
is the identity.
p17.1.5
Say f: a > a
. Then αA * Ff = Gf * αA
.
Section 18: Adjunctions
p18.1 Derive the naturality square for ψ
, the transformation between the two (contravariant) functors:
a > C(L a, b)
a > D(a, R b)
Solution Say we have
f :: a1 > a2
F f :: C(L a1, b) > C(L a2, b)
G f :: D(a1, R b) > D(a2, R b)
Where L
and R
are functors
L :: D(a, R b) > C(L a, b)
R :: C(L a, b) > D(a, R b)
and define the natural transformation ψ: G > F
such that
ψg1 :: D(a1, R b) > C(L a1, b)
ψg2 :: D(a2, R b) > C(L a2, b)
Now consider the morphisms g1: a1 > R
and g2: a2 > R b
. We want to prove that F f * ψg1 = ψg2 * G f
ψg2 * G f G a1 = // definition of f
ψg2 * G a2 = // definition of G
ψg2 * D(a2, R b) = // definition of ψg2
C(L a2, b) = // definition of G
F a2 = // definition of F
F f * F a1 = // definition of f
F f * ψg1 G a1 // definition of ψg1
p18.2 Derive the counit ε
starting from the homsets isomorphism in the second definition of the adjunction.
Solution
Assume that C(L d, c) ≅ D(d, R c)
holds for any c
in C
and d
in D
. We want to prove that there exists some natural transformation ε :: L . R > Ic
.
Say d = R c
, then C((L . R) c, c) ≅ D(R c, R c)
. Since D(Rc, Rc)
contains at least the identity, our natural transformation from D(R c, R c) > C((L . R) c, I c)
must map to a nonempty set. Therefore, we have some set of morphisms that map from (L . R) c > I c
for any c
. These morphisms form a natural transformation from L * R > Ic
, which is ε
.
p18.3 Complete the proof of equivalence of the two definitions of the adjunction.
Solution
In order to prove that the two definitions are equivalent, we need to prove the equivalence of the isomorphism C(L d, c) ≅ D(d, R c)
and the existence of two natural transformations: the unit η
and the counit ε
.
In the text we proved that C(L d, c) ≅ D(d, R c)
implies the existence of the η
and that the existence of the η
and ε
implies the existence of a mapping from C(L d, c)
to D(d, R c)
. In p18.2, we proved that C(L d, c) ≅ D(d, R c)
implies the existence of the ε
. Therefore, we still need to prove that the existence of the η
and ε
implies the existence of ψ :: D(d, R c) > C(L d, c)
For some morphism f :: d > R c
, we can apply εc * L
to form the morphism:
εc . L f =
L d > εc L . R c =
L d > c =
ψf
p18.4/5 Show that the coproduct can be defined by an adjunction. Start with the definition of the factorizer for a coproduct. Show that the coproduct is the left adjoint of the diagonal functor.
Solution
(In this solution, we assume C is Set or Hask)
We want to prove that C(Either a b, c) ≅ (C × C)(<a, b>, Δ c)
. A homset in CxC
is (C×C)(<a, b>, Δ c)
, which consists of pairs of functions a > c, b > c
and a homset in C
is C(Either a b, c)
, which consists of functions (Either a b > c)
We can define a natural transformation between these two homsets with the factorizer
and inversefactorizer
functions.
factorizer :: (C×C)(<a, b>, Δ c) > C(Either a b, c)
inversefactorizer :: C(Either a b, c) > (C×C)(<a, b>, Δ c)
We can write these in pseudohaskell as
factorizer :: ((a > c), (b > c)) > (Either a b > c)
factorizer (i,j) (Left a) = i a
factorizer (i,j) (Right b) = j b
inversefactorizer :: (Either a b > c) > ((a > c), (b > c))
inversefactorizer m = (\a > m Left a), (\b > m Right b)
Now note that because these are both polymorphic in a,b,c
, both factorizer
and inversefactorizer
are natural, so C(Either a b, c) ≅ (C × C)(<a, b>, Δ c)
.
p18.6 Define the adjunction between a product and a function object in Haskell.
Solution
producttofunction :: ((z, a) > b) > (z > (a > b))
producttofunction f = \z > (\a > f (z,a))
functiontoproduct :: (z > (a > b)) > ((z, a) > b)
functiontoproduct f = \z_a > ((f (fst z_a)) (snd z_a))
Section 19: Free/Forgetful Adjunctions
p19.1 Consider a free monoid built from a singleton set as its generator. Show that there is a onetoone correspondence between morphisms from this free monoid to any monoid m
, and functions from the singleton set to the underlying set of m`.
Solution
Forward
Consider a morphism from the free monoid with the singleton set as its generator to m
. This morphism maps the generator element e
to some m1
in m
. There exists exactly one function in the homset between the singleton set and the underlying set of m
that maps ()
to m1
, so we can define a forward mapping.
Backward
Consider a function from the singleton set to the underlying set of m
. This function “chooses” a single element m1
from the underlying set of m
. We can define exactly one homomorphism between the singleton free monoid and m
that maps the generator element e
to m1
, since any such homomorphism must satisfy the following:
1 > unit
e > m1
ee > m1m1
eee > m1m1m1
so there is exactly one such homomorphism and we can define a backward mapping.
Section 20: Monads: Programmer’s Definition (No Challenges)
Section 21: Monads and Effects (No Challenges)
Section 22: Monads Categorically (No Challenges)
Section 24: FAlgebras
p24.1 Implement the evaluation function for a ring of polynomials of one variable. You can represent a polynomial as a list of coefficients in front of powers of x. For instance, 4x^21
would be represented as (starting with the zero’th power) [1, 0, 4]
.
Solution
polyEval :: [Double] > Double > Double
polyEval coefficients value = foldr (\ (power, coeff) sumSoFar > sumSoFar + coeff * (value ** power)) 0.0 (zip [0..] coefficients)
isTrue = 99.0 == (polyEval [1, 0, 4] 5)
p24.2 Generalize the previous construction to polynomials of many independent variables, like x^2y3y^3z
.
Solution
raiseAndProd :: [Double] > [Double] > Double
raiseAndProd values powers = foldr (\(value, power) prodSoFar > prodSoFar * (value ** power)) 1.0 (zip values powers)
polyMultiEval :: [(Double, [Double])] > [Double] > Double
polyMultiEval coeffsExps values = foldr (\ (coeff, powers) sumSoFar > sumSoFar + coeff * (raiseAndProd values powers)) 0.0 coeffsExps
isTrue1 = 2580.0 == (polyMultiEval [(1, [2, 1, 0]), (3, [0, 3, 1])] [3, 5, 7]))
isTrue2 = 1.0 == (polyMultiEval [(1, [2, 1])] [1, 1])
p24.3 Implement the algebra for the ring of 2×2
matrices.
Solution
data MatExpr = RZero
 ROne
 RCompA
 RCompB
 RCompC
 RCompD
 RAdd MatExpr MatExpr
 RMul MatExpr MatExpr
 RNeg MatExpr
type MatrixTwoTwo = (Double, Double, Double, Double)
mCompA :: MatrixTwoTwo
mCompA = (1, 0, 0, 0)
mCompB :: MatrixTwoTwo
mCompB = (0, 1, 0, 0)
mCompC :: MatrixTwoTwo
mCompC = (0, 0, 1, 0)
mCompD :: MatrixTwoTwo
mCompD = (0, 0, 0, 1)
mZero :: MatrixTwoTwo
mZero = (0, 0, 0, 0)
mEye :: MatrixTwoTwo
mEye = (1, 0, 1, 0)
mAdd :: MatrixTwoTwo > MatrixTwoTwo > MatrixTwoTwo
mAdd (a1,b1,c1,d1) (a2,b2,c2,d2) = (a1 + a2, b1 + b2, c1 + c2, d1 + d2)
mMult :: MatrixTwoTwo > MatrixTwoTwo > MatrixTwoTwo
mMult (a1,b1,c1,d1) (a2,b2,c2,d2) = (a1 * a2 + b1 * c2, a1 * b2 + b1 * d2, c1 * a2 + d1 * c2, c1 * b2 + d1 * d2)
mNeg :: MatrixTwoTwo > MatrixTwoTwo
mNeg (a1,b1,c1,d1) = (a1,b1,c1,d1)
evalZ :: MatExpr > MatrixTwoTwo
evalZ RZero = mZero
evalZ ROne = mEye
evalZ RCompA = mCompA
evalZ RCompB = mCompB
evalZ RCompC = mCompC
evalZ RCompD = mCompD
evalZ (RAdd e1 e2) = mAdd (evalZ e1) (evalZ e2)
evalZ (RMul e1 e2) = mMult (evalZ e1) (evalZ e2)
evalZ (RNeg e) = mNeg (evalZ e)
matrixExpression :: MatExpr
matrixExpression = RMul (RAdd RCompA RCompB) (RAdd RCompC RCompD)
isTrue = (1.0, 1.0, 0.0, 0.0) == (evalZ matrixExpression))
p24.4 Define a coalgebra whose anamorphism produces a list of squares of natural numbers.
Solution
newtype Fix f = Fix (f (Fix f))
unFix :: Fix f > f (Fix f)
unFix (Fix x) = x
cata :: Functor f => (f a > a) > Fix f > a
cata alg = alg . fmap (cata alg) . unFix
ana :: Functor f => (a > f a) > a > Fix f
ana coalg = Fix . fmap (ana coalg) . coalg
data StreamF e a = StreamF e a deriving Functor
toListC :: Fix (StreamF e) > [e]
toListC = cata al
where al :: StreamF e [e] > [e]
al (StreamF e a) = e : a
nat :: [Int] > StreamF Int [Int]
nat (p : ns) = StreamF (p^2) ns
squaresStream :: Fix (StreamF Int)
squaresStream = ana nat [0..]
squaresList :: [Int]
squaresList = toListC squaresStream
p24.5 Use unfoldr
to generate a list of the first n primes.
Solution
listSieve :: [Int] > Maybe (Int, [Int])
listSieve (p : ns) = Just (p, (filter (notdiv p) ns))
where notdiv p n = n `mod` p /= 0
primeFilteredList :: [Int]
primeFilteredList = unfoldr listSieve [2..]
isTrue1 = primeFilteredList!!0 == 2
isTrue2 = primeFilteredList!!3 == 7
Section 25: Algebras for Monads
p25.1 What is the action of the free functor F :: C > C^T
on morphisms. Hint: use the naturality condition for monadic μ
.
Solution
First, note that F a = (T a, μa)
. Since μ
is natural, we see that T f . μa = μb . (T . T) f
. Therefore, for some f :: a > b
, the action of F
on f
is:
fmap f (T a, μa) = (fmap f T a, T f . μa)
p25.2 Define the adjunction: U^W ⊣ F^W
Solution
First, we define the unit η :: I > F^W . U^W
. Since it’s the case that
F^W . U^W (W a, f) =
F^W (W a) =
(W W a, δWa)
η
needs to map (W a, f) > (W W a, δWa)
. We can accomplish this by using f
, the coevaluator of the coalgebra, to define the component of η
at (W a, f)
Next, we define the counit ε :: U^W . F^W > I
. Since it’s the case that:
U^W . F^W a =
U^W . (W a, δa) =
W a
ε
needs to map W a > a
so we can use the extract
method of the comonad to define the component of ε
at Wa
.
p25.3 Prove that the above adjunction reproduces the original comonad.
Solution
First, we can use the counit of the adjunction ε
as the comonadic extract, since εWa W a = a
Next, we can use the unit of the adjunction to define the comonadic duplicate
as the horizontal composition of three natural transformations U^W ◦ η ◦ F^W
where U^W: U^W > U^W
and F^W: F^W > F^W
. Since F^W
lifts a
to (W a, δa)
, η
picks the coevaluator δa
which maps W a > W W a
and U^W
has no action on morphisms, we see that duplicate = U^W ◦ η ◦ F^W
.
Section 26: Ends and Coends (No Challenges)
Section 27: Kan Extensions (No Challenges)
Section 28: Enriched Categories (No Challenges)
Section 29: Topoi
p29.1 Show that the function f
that is the pullback of true
along the characteristic function must be injective.
Solution
If f: a > b
is the pullback of true
along the characteristic function, then for any a*
, f*: a* > b
, there exists some unique h: a* > a
such that f* = f . h
Consider the case where a*
is the image of f
and f*
is the identity. If f
is not injective, then for the e1, e2, e1 != e2
in a
such that f(e1) = f(e2)
, h
can map f(e1) = f(e2)
to either e1
or e2
. Therefore h
would not be unique, which implies that f
must be injective.
Section 30: Lawvere Theories
p30.1 Enumarate all morphisms between 2
and 3
in F
(the skeleton of FinSet
).
Solution
(0>0, 1>0), (0>0, 1>1), (0>0, 1>2), (0>1, 1>0), (0>1, 1>1), (0>1, 1>2)
p30.2 Show that the category of models for the Lawvere theory of monoids is equivalent to the category of monad algebras for the list monad.
Solution
First, let’s note that the category of models of the Lawvere theory for monoids is equivalent to the category of all monoids, Mon
. Now we will prove that Mon
is equivalent to the category of monad algebras for the list monad.
First, given a monoid over the set a
, we can produce an algebra (a, f)
where f
maps the list L
to the monoidal product of the elements in L
. Next, given an algebra (a, f)
, we can produce a monoid over a
by defining the monoidal product of a1, a2
to be f ([a1] cat [a2])
. The unit of this monoid is []
, and because of the monad condition f . μa = f . T f
we see that:
f [a1, f [a2, a3]] =
f [a1, a2, a3] =
f [f [a1, a2], a3]
So the monoid associativity law is automatically satisfied.
p30.3 The Lawvere theory of monoids generates the list monad. Show that its binary operations can be generated using the corresponding Kleisli arrows.
Solution
The binary operations in the Lawvere theory of monoids are elements of the homset LMon(2, 1)
, which are functions of two arguments that we can implement with only the monoidal operator. Each of these functions can be defined by a list composed of only those 2 unique elements. Since each Kleisli arrow in the homset KlT (1, 2)
corresponds to a list composed of elements from the 2 element set, we can represent each binary operation in the Lawvere theory of monoids with a Kleisli arrow in KlT (1, 2)
.
p30.4 FinSet
is a subcategory of Set
and there is a functor that embeds it in Set
. Any functor on Set
can be restricted to FinSet
. Show that a finitary functor is the left Kan extension of its own restriction
Solution
Say K: Set > FinSet
is a functor that embeds a set into FinSet
, such that for any finite set n
in Set
, K n = n
. Then FinSet(K n, a)
is a homset between elements in FinSet
, and so FinSet(K n, a) = a^(K n) = a^n
.
Now consider some finitary functor F
. The left Kan extension of F
’s restriction to FinSet
along K
is
LanK F a =
∫^n FinSet(K n, a) × F n =
∫^n a^n × F n = \\ definition of finitary functor
F
So a finitary functor is the left Kan extension of its own restriction.
Section 31: Monads, Monoids, and Categories
p31.1 Derive unit and associativity laws for the tensor product defined as composition of endo1cells in a bicategory.
Solution
Unit Law
The left and right compositions of any endo1cell T
and the identity 1cell id
are T . id
and id . T
. By the definition of a bicategory there exist invertible 2cells mapping each of these endo1cells to T
.
Associativity Law
Given three endo1cells T1, T2, T3
, by the definition of a bicategory there exists an invertible 2cell that maps between ((T1 . T2) . T3
and T1 . (T2 . T3)
.
p31.2 Check that monad laws for a monad in Span
correspond to identity and associativity laws in the resulting category.
Solution
A monad in Span consists of an endo1cell that has the sets Ar, Ob
with the functions
dom :: Ar > Ob
cod :: Ar > Ob
and the associated 2cells:
μ: Ar x Ar > Ar
η: Ob > Ar
This monad defines a category consisting of the objects in Ob
and the arrows in Ar
, where each arrow in Ar
connects dom Ar
to cod Ar
.
Identity
η
assigns an identity arrow to each object such that
dom . η = id
cod . η = id
Therefore for any object o1
in Ob
and arrow a1
in Ar
where cod a1 = o1
, we see that:
dom (μ (a1, η o1)) = dom a1
cod (μ (a1, η o1)) = cod (η o1) = o1 = cod a1
So the composition of an arrow with the identity arrow does not change that arrow’s domain or codomain.
Associativity
By the monoid law for μ
μ (Ar x μ (Ar x Ar)) = μ (μ (Ar x Ar) x Ar)
Therefore, for any arrows a1, a2, a3
, we see that
a1 . (a2 . a3) = (a1 . a2) . a3
p31.3 Show that a monad in Prof
is an identityonobjects functor.
Solution
In Prof
, we define a monad with an endoprofunctor T
such that T: Cop x C > Set
. The composition of profunctors is
(q . p) a b = ∫^c p c a × q b c
So the composition of T
with itself is:
(T . T) C C =
∫^c T c C × T C c = // existential quantifier
T C C
Which implies that T
must map each object in C
to itself.
p31.4 What’s a monad algebra for a monad in Span
?
Solution
Given a monad m
over some object a
, we form an algebra over this monad with a map alg :: m a > a
that satisfies commutativity conditions. For a monad in Span
, we can use dom
or cod
for alg
.
Identity alg . ηa = ida
This holds by the definition of η
for Span
Associativity alg . μa = alg . m alg
Without loss of generality, we can see the following:
dom . μa (a1, a2) =
dom a1 =
dom . m dom (a1, a2)