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Convex Optimization
Solutions Manual
Stephen Boyd Lieven Vandenberghe
January 4, 2006

Chapter 2
Convex sets

Exercises
Exercises
Definition of convexity
2.1 Let C ⊆ R
n
be a convex set, with x
1
, . . . , x
k
∈ C, and let θ
1
, . . . , θ
k
∈ R satisfy θ
i
≥ 0,
θ
1
+ ··· + θ
k
= 1. Show that θ
1
x
1
+ ··· + θ
k
x
k
∈ C. (The definition of convexity is that
this holds for k = 2; you must show it for arbitrary k.) Hint. Use induction on k.
Solution. This is readily shown by induction from the definition of convex set. We illus-
trate the idea for k = 3, leaving the general case to the reader. Suppose that x
1
, x
2
, x
3
∈ C,
and θ
1
+ θ
2
+ θ
3
= 1 with θ
1
, θ
2
, θ
3
≥ 0. We will show that y = θ
1
x
1
+ θ
2
x
2
+ θ
3
x
3
∈ C.
At least one of the θ
i
is not equal to one; without loss of generality we can assume that
θ
1
6= 1. Then we can write
y = θ
1
x
1
+ (1 − θ
1
)(µ
2
x
2
+ µ
3
x
3
)
where µ
2
= θ
2
/(1 − θ
1
) and µ
2
= θ
3
/(1 − θ
1
). Note that µ
2
, µ
3
≥ 0 and
µ
1
+ µ
2
=
θ
2
+ θ
3
1 − θ
1
=
1 − θ
1
1 − θ
1
= 1.
Since C is convex and x
2
, x
3
∈ C, we conclude that µ
2
x
2
+ µ
3
x
3
∈ C. Since this point
and x
1
are in C, y ∈ C.
2.2 Show that a set is convex if and only if its intersection with any line is convex. Show that
a set is affine if and only if its intersection with any line is affine.
Solution. We prove the first part. The intersection of two convex sets is convex. There-
fore if S is a convex set, the intersection of S with a line is convex.
Conversely, suppose the intersection of S with any line is convex. Take any two distinct
points x
1
and x
2
∈ S. The intersection of S with the line through x
1
and x
2
is convex.
Therefore convex combinations of x
1
and x
2
belong to the intersection, hence also to S.
2.3 Midpoint convexity. A set C is midpoint convex if whenever two points a, b are in C, the
average or midpoint (a + b)/2 is in C. Obviously a convex set is midpoint convex. It can
be proved that under mild conditions midpoint convexity implies convexity. As a simple
case, prove that if C is closed and midpoint convex, then C is convex.
Solution. We have to show that θx + (1 − θ)y ∈ C for all θ ∈ [0, 1] and x, y ∈ C. Let
θ
(k)
be the binary number of length k, i.e., a number of the form
θ
(k)
= c
1
2
−1
+ c
2
2
−2
+ ··· + c
k
2
−k
with c
i
∈ {0, 1}, closest to θ. By midpoint convexity (applied k times, recursively),
θ
(k)
x + (1 − θ
(k)
)y ∈ C. Because C is closed,
lim
k→∞
(θ
(k)
x + (1 − θ
(k)
)y) = θx + (1 − θ)y ∈ C.
2.4 Show that the convex hull of a set S is the intersection of all convex sets that contain S.
(The same method can be used to show that the conic, or affine, or linear hull of a set S
is the intersection of all conic sets, or affine sets, or subspaces that contain S.)
Solution. Let H be the convex hull of S and let D be the intersection of all convex sets
that contain S, i.e.,
D =
\
{D | D convex, D ⊇ S}.
We will show that H = D by showing that H ⊆ D and D ⊆ H.
First we show that H ⊆ D. Suppose x ∈ H, i.e., x is a convex combination of some
points x
1
, . . . , x
n
∈ S. Now let D be any convex set such that D ⊇ S. Evidently, we have
x
1
, . . . , x
n
∈ D. Since D is convex, and x is a convex combination of x
1
, . . . , x
n
, it follows
that x ∈ D. We have shown that for any convex set D that contains S, we have x ∈ D.
This means that x is in the intersection of all convex sets that contain S, i.e., x ∈ D.
Now let us show that D ⊆ H. Since H is convex (by definition) and contains S, we must
have H = D for some D in the construction of D, proving the claim.

2 Convex sets
Examples
2.5 What is the distance between two parallel hyperplanes {x ∈ R
n
| a
T
x = b
1
} and {x ∈
R
n
| a
T
x = b
2
}?
Solution. The distance between the two hyperplanes is |b
1
− b
2
|/kak
2
. To see this,
consider the construction in the figure below.
PSfrag replacements
a
x
1
= (b
1
/kak
2
)a
x
2
= (b
2
/kak
2
)a
a
T
x = b
2
a
T
x = b
1
The distance between the two hyperplanes is also the distance between the two points
x
1
and x
2
where the hyperplane intersects the line through the origin and parallel to the
normal vector a. These points are given by
x
1
= (b
1
/kak
2
2
)a, x
2
= (b
2
/kak
2
2
)a,
and the distance is
kx
1
− x
2
k
2
= |b
1
− b
2
|/kak
2
.
2.6 When does one halfspace contain another? Give conditions under which
{x | a
T
x ≤ b} ⊆ {x | ˜a
T
x ≤
˜
b}
(where a 6= 0, ˜a 6= 0). Also find the conditions under which the two halfspaces are equal.
Solution. Let H = {x | a
T
x ≤ b} and
˜
H = {x | ˜a
T
x ≤
˜
b}. The conditions are:
• H ⊆
˜
H if and only if there exists a λ > 0 such that ˜a = λa and
˜
b ≥ λb.
• H =
˜
H if and only if there exists a λ > 0 such that ˜a = λa and
˜
b = λb.
Let us prove the first condition. The condition is clearly sufficient: if ˜a = λa and
˜
b ≥ λb
for some λ > 0, then
a
T
x ≤ b =⇒ λa
T
x ≤ λb =⇒ ˜a
T
x ≤
˜
b,
i.e., H ⊆
˜
H.
To prove necessity, we distinguish three cases. First suppose a and ˜a are not parallel. This
means we can find a v with ˜a
T
v = 0 and a
T
v 6= 0. Let ˆx be any point in the intersection
of H and
˜
H, i.e., a
T
ˆx ≤ b and ˜a
T
x ≤
˜
b. We have a
T
(ˆx + tv) = a
T
ˆx ≤ b for all t ∈ R.
However ˜a
T
(ˆx + tv) = ˜a
T
ˆx + t˜a
T
v, and since ˜a
T
v 6= 0, we will have ˜a
T
(ˆx + tv) >
˜
b for
sufficiently large t > 0 or sufficiently small t < 0. In other words, if a and ˜a are not
parallel, we can find a point ˆx + tv ∈ H that is not in
˜
H, i.e., H 6⊆
˜
H.
Next suppose a and ˜a are parallel, but point in opposite directions, i.e., ˜a = λa for some
λ < 0. Let ˆx be any point in H. Then ˆx−ta ∈ H for all t ≥ 0. However for t large enough
we will have ˜a
T
(ˆx − ta) = ˜a
T
ˆx + tλkak
2
2
>
˜
b, so ˆx − ta 6∈
˜
H. Again, this shows H 6⊆
˜
H.

Exercises
Finally, we assume ˜a = λa for some λ > 0 but
˜
b < λb. Consider any point ˆx that satisfies
a
T
ˆx = b. Then ˜a
T
ˆx = λa
T
ˆx = λb >
˜
b, so ˆx 6∈
˜
H.
The proof for the second part of the problem is similar.
2.7 Voronoi description of halfspace. Let a and b be distinct points in R
n
. Show that the set
of all points that are closer (in Euclidean norm) to a than b, i.e., {x | kx−ak
2
≤ kx−bk
2
},
is a halfspace. Describe it explicitly as an inequality of the form c
T
x ≤ d. Draw a picture.
Solution. Since a norm is always nonnegative, we have kx − ak
2
≤ kx − bk
2
if and only
if kx − ak
2
2
≤ kx − bk
2
2
, so
kx − ak
2
2
≤ kx − bk
2
2
⇐⇒ (x − a)
T
(x − a) ≤ (x − b)
T
(x − b)
⇐⇒ x
T
x − 2a
T
x + a
T
a ≤ x
T
x − 2b
T
x + b
T
b
⇐⇒ 2(b − a)
T
x ≤ b
T
b − a
T
a.
Therefore, the set is indeed a halfspace. We can take c = 2(b − a) and d = b
T
b − a
T
a.
This makes good geometric sense: the points that are equidistant to a and b are given by
a hyperplane whose normal is in the direction b − a.
2.8 Which of the following sets S are polyhedra? If possible, express S in the form S =
{x | Ax b, F x = g}.
(a) S = {y
1
a
1
+ y
2
a
2
| − 1 ≤ y
1
≤ 1, − 1 ≤ y
2
≤ 1}, where a
1
, a
2
∈ R
n
.
(b) S = {x ∈ R
n
| x 0, 1
T
x = 1,
P
n
i=1
x
i
a
i
= b
1
,
P
n
i=1
x
i
a
2
i
= b
2
}, where
a
1
, . . . , a
n
∈ R and b
1
, b
2
∈ R.
(c) S = {x ∈ R
n
| x 0, x
T
y ≤ 1 for all y with kyk
2
= 1}.
(d) S = {x ∈ R
n
| x 0, x
T
y ≤ 1 for all y with
P
n
i=1
|y
i
| = 1}.
Solution.
(a) S is a polyhedron. It is the parallelogram with corners a
1
+ a
2
, a
1
− a
2
, −a
1
+ a
2
,
−a
1
− a
2
, as shown below for an example in R
2
.
PSfrag replacements
a
1
a
2
c
2
c
1
For simplicity we assume that a
1
and a
2
are independent. We can express S as the
intersection of three sets:
• S
1
: the plane defined by a
1
and a
2
• S
2
= {z + y
1
a
1
+ y
2
a
2
| a
T
1
z = a
T
2
z = 0, −1 ≤ y
1
≤ 1}. This is a slab parallel to
a
2
and orthogonal to S
1
• S
3
= {z + y
1
a
1
+ y
2
a
2
| a
T
1
z = a
T
2
z = 0, −1 ≤ y
2
≤ 1}. This is a slab parallel to
a
1
and orthogonal to S
1
Each of these sets can be described with linear inequalities.
• S
1
can be described as
v
T
k
x = 0, k = 1, . . . , n − 2
where v
k
are n −2 independent vectors that are orthogonal to a
1
and a
2
(which
form a basis for the nullspace of the matrix [a
1
a
2
]
T
).
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