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LP Duality
John E. Mitchell
Department of Mathematical Sciences
RPI, Troy, NY 12180 USA
January 2016
Mitchell LP Duality 1 / 26
Outline
1 Weak duality
2 Strong duality
3 Complementary slackness
4 Farkas Lemma
5 Visualizing the Farkas Lemma
Mitchell LP Duality 2 / 26
Outline
LP dual pair
We work with the standard form linear program and its dual:
minx2IRn cT x maxy2IRm bT ysubject to Ax = b (P) subject to AT y c (D)
x � 0
where c 2 IRn, b 2 IRm, and A 2 IRm⇥n. The results extend to any
primal-dual pair, including
minx2IRn cT x maxy2IRm bT ysubject to Ax � b (P) subject to AT y c (D)
x � 0 y � 0
Mitchell LP Duality 3 / 26
Weak duality
Outline
1 Weak duality
2 Strong duality
3 Complementary slackness
4 Farkas Lemma
5 Visualizing the Farkas Lemma
Mitchell LP Duality 4 / 26
Weak duality
Weak duality
Theorem
Weak duality: If x is feasible in (P) and y is feasible in (D) thencT x � bT y.
Proof.
We have
bT y = (Ax)T y = xT AT y xT c = cT x
where the inequality follows because x � 0 and AT y c.
Mitchell LP Duality 5 / 26
Weak duality
Weak duality
Theorem
Weak duality: If x is feasible in (P) and y is feasible in (D) thencT x � bT y.
Proof.
We have
bT y = (Ax)T y = xT AT y xT c = cT x
where the inequality follows because x � 0 and AT y c.
Mitchell LP Duality 5 / 26
Arab, 5 7 0 AtfE c
Weak duality
Sufficient optimality criterion
As an immediate consequence of the weak duality theorem, we have
the following sufficient condition for optimality:
Theorem
Sufficient optimality criterion: If x is primal feasible and y is dualfeasible and cT x = bT y then x is optimal in (P) and y is optimal in (D).
Mitchell LP Duality 6 / 26
Strong duality
Outline
1 Weak duality
2 Strong duality
3 Complementary slackness
4 Farkas Lemma
5 Visualizing the Farkas Lemma
Mitchell LP Duality 7 / 26
Strong duality
Strong duality
This condition is also necessary:
Theorem
Strong duality theorem: If (P) is feasible with a finite optimal valuethen (D) is also feasible. Further, there exist optimal solutions x⇤ andy⇤ with cT x⇤ = bT y⇤.
We will prove this theorem later. In particular, we will use the simplex
algorithm to show how a dual optimal solution can be constructed with
cT x⇤ = bT y⇤.
Mitchell LP Duality 8 / 26
Strong duality
Possible states of an optimization problem
A general optimization problem can be in one of four possible states:
Infeasible
Feasible with an unbounded optimal value.
Feasible with a finite optimal value that is attained.
Feasible with a finite optimal value that is not attained.
It follows from the strong duality theorem that this last case is not
possible for a linear program. The strong duality theorem also implies
that the state of a linear program tells us the state of its dual linear
program. So a primal-dual pair can only be in one of 4 possible states,
rather than 4 ⇥ 4 = 16 possible states.
Mitchell LP Duality 9 / 26
Strong duality
Possible states of an optimization problem
A general optimization problem can be in one of four possible states:
Infeasible
Feasible with an unbounded optimal value.
Feasible with a finite optimal value that is attained.
Feasible with a finite optimal value that is not attained.
It follows from the strong duality theorem that this last case is not
possible for a linear program. The strong duality theorem also implies
that the state of a linear program tells us the state of its dual linear
program. So a primal-dual pair can only be in one of 4 possible states,
rather than 4 ⇥ 4 = 16 possible states.
Mitchell LP Duality 9 / 26
Strong duality
Possible states of an optimization problem
A general optimization problem can be in one of four possible states:
Infeasible
Feasible with an unbounded optimal value.
Feasible with a finite optimal value that is attained.
Feasible with a finite optimal value that is not attained.
It follows from the strong duality theorem that this last case is not
possible for a linear program. The strong duality theorem also implies
that the state of a linear program tells us the state of its dual linear
program. So a primal-dual pair can only be in one of 4 possible states,
rather than 4 ⇥ 4 = 16 possible states.
Mitchell LP Duality 9 / 26
Strong duality
Possible states of an optimization problem
A general optimization problem can be in one of four possible states:
Infeasible
Feasible with an unbounded optimal value.
Feasible with a finite optimal value that is attained.
Feasible with a finite optimal value that is not attained.
It follows from the strong duality theorem that this last case is not
possible for a linear program. The strong duality theorem also implies
that the state of a linear program tells us the state of its dual linear
program. So a primal-dual pair can only be in one of 4 possible states,
rather than 4 ⇥ 4 = 16 possible states.
Mitchell LP Duality 9 / 26
r u i n e - ×i t . x 2 0
I
Strong duality
Possible states of an LP pair
In particular, we have the following consequence of the strong duality
theorem:
Theorem
The following are mutually exclusive and exhaustive possibilities for(P) and (D):
(P) and (D) are both infeasible.(D) is infeasible and (P) has an unbounded optimal value.(P) is infeasible and (D) has an unbounded optimal value.Both (P) and (D) are feasible, and they have the same optimalvalue.
Mitchell LP Duality 10 / 26
Strong duality
Possible states of an LP pair
In particular, we have the following consequence of the strong duality
theorem:
Theorem
The following are mutually exclusive and exhaustive possibilities for(P) and (D):
(P) and (D) are both infeasible.(D) is infeasible and (P) has an unbounded optimal value.(P) is infeasible and (D) has an unbounded optimal value.Both (P) and (D) are feasible, and they have the same optimalvalue.
Mitchell LP Duality 10 / 26
Strong duality
Possible states of an LP pair
In particular, we have the following consequence of the strong duality
theorem:
Theorem
The following are mutually exclusive and exhaustive possibilities for(P) and (D):
(P) and (D) are both infeasible.(D) is infeasible and (P) has an unbounded optimal value.(P) is infeasible and (D) has an unbounded optimal value.Both (P) and (D) are feasible, and they have the same optimalvalue.
Mitchell LP Duality 10 / 26
Strong duality
Possible states of an LP pair
In particular, we have the following consequence of the strong duality
theorem:
Theorem
The following are mutually exclusive and exhaustive possibilities for(P) and (D):
(P) and (D) are both infeasible.(D) is infeasible and (P) has an unbounded optimal value.(P) is infeasible and (D) has an unbounded optimal value.Both (P) and (D) are feasible, and they have the same optimalvalue.
Mitchell LP Duality 10 / 26
Strong duality
Possible states of an LP pair
In particular, we have the following consequence of the strong duality
theorem:
Theorem
The following are mutually exclusive and exhaustive possibilities for(P) and (D):
(P) and (D) are both infeasible.(D) is infeasible and (P) has an unbounded optimal value.(P) is infeasible and (D) has an unbounded optimal value.Both (P) and (D) are feasible, and they have the same optimalvalue.
Mitchell LP Duality 10 / 26
Complementary slackness
Outline
1 Weak duality
2 Strong duality
3 Complementary slackness
4 Farkas Lemma
5 Visualizing the Farkas Lemma
Mitchell LP Duality 11 / 26
Complementary slackness
Complementary slackness
Theorem
A pair of primal and dual feasible solutions are optimal to theirrespective problems in a primal-dual pair of LPs if and only if
whenever these variables make a slackvariable in one problem strictly positivethe value of the associated nonnegativevariable in the other is zero.
9>>=
>>;
complementaryslackness
Mitchell LP Duality 12 / 26
m m × ,-12×12+34, (p)Optimal:
S t . × , -1×2++3=1 ×*=(1,0,O)x i2 0 Value : I
5 1=0m a x ys e . y@tight.
Slacker
y e a(D) Optimal:
yes}slack>o y-4=1,f - O , value: 1 .
y free s , >0
Complementary,lactic,,: s , >o , s , >oS i x t o
⇒ x x x ,-Oft i
Complementary slackness
Proof of complementary slackness
We prove this for the standard pair (P) and (D). We can define the
vector of dual slacks s = c � AT y 2 IRn for any y 2 IRm. Note that the
duality gap is
cT x � bT y = cT x � (Ax)T y = cT x � xT AT y = cT x � (AT y)T x
= (c � AT y)T x = sT x =nX
i=1
sixi
for any y 2 IRm and x 2 IRn with Ax = b.
Mitchell LP Duality 13 / 26
Complementary slackness
Proof of complementary slackness continued
Note that if x and y are feasible in their respective problems then x � 0
and s � 0, so sT x � 0.
If the points are optimal then cT x � bT y = 0 soPn
i=1sixi = 0, so each
component sixi = 0, since they must all be nonnegative. So either
si = 0 or xi = 0 for each component i . This is complementary
slackness.
If complementary slackness holds then either xi = 0 or si = 0 for each
component i , so sT x = 0 so cT x = bT y so the points are optimal. ⇤
Mitchell LP Duality 14 / 26
Complementary slackness
Complementary slackness for standard primal-dual
pair
Exercise:
Consider the primal-dual pair
minx cT x maxy bT ysubject to Ax � b (P) subject to AT y c (D)
x � 0 y � 0
and let s = c � AT y , w = Ax � b. Show cT x � bT y = xT s + yT w .
Mitchell LP Duality 15 / 26
T TExisi S yi w i
Farkas Lemma
Outline
1 Weak duality
2 Strong duality
3 Complementary slackness
4 Farkas Lemma
5 Visualizing the Farkas Lemma
Mitchell LP Duality 16 / 26
Farkas Lemma
Farkas Lemma
Theorem
Farkas Lemma: Let A 2 IRm⇥n and b 2 IRm. Exactly one of thefollowing two systems has a solution:
(I): Ax = b, x � 0. (II): AT y 0, bT y > 0.
Mitchell LP Duality 17 / 26
Farkas Lemma
Farkas Lemma
Theorem
Farkas Lemma: Let A 2 IRm⇥n and b 2 IRm. Exactly one of thefollowing two systems has a solution:
(I): Ax = b, x � 0. (II): AT y 0, bT y > 0.
Mitchell LP Duality 17 / 26
Farkas Lemma
Proof of Farkas Lemma
Proof.
Prove using LP duality. Set up the primal-dual pair (P) and (D) with
c = 0. Note that y = 0 is feasible in (D), so (D) either has finite
optimal value or unbounded optimal value. Note also that (P) is either
infeasible or has optimal value equal to zero.
If (I) is consistent then (P) has optimal value 0 so (D) also has optimal
value 0, so there is no y 2 IRm with both AT y 0 and bT y > 0, so (II)is inconsistent.
If (I) is inconsistent then (P) is infeasible so (D) has unbounded
optimal value, so there exists y with AT y 0 and bT y > 0, so (II) is
consistent.
Mitchell LP Duality 18 / 26
( I ) Axeb.→ O (II) A' je 0 ,big>o(p) m i n O x (D) m a x bey
s e . Ax-b s - t . AlysO÷( I ) consistent ⇒ (P)feasible,optimal valueof O
⇒ (D)feasible, optimalvalue0 ⇒ ( I l ) inconsistent.
-( I ) inconsistent ⇒ (P)
infeasible
⇒ either: (D) infeasible-(D)alwan,feasible:
° ' : ( D ) unboundedt a key-OER"
⇒ (D) unbounded ⇒ I T will Atyeoandbig>
o .⇒ ( I )consistent.
Visualizing the Farkas Lemma
Outline
1 Weak duality
2 Strong duality
3 Complementary slackness
4 Farkas Lemma
5 Visualizing the Farkas Lemma
Mitchell LP Duality 19 / 26
Visualizing the Farkas Lemma
The columns of A
Let
A =
1 3 2
4 2 1
�.
For what vectors b 2 IR2 is System I consistent?
For what b 2 IR2 is System II consistent?
Mitchell LP Duality 20 / 26
Visualizing the Farkas Lemma
Consistency of System I
For System I, we require b = Ax for some x � 0.
We can express Ax as follows:
Ax =
1 3 2
4 2 1
�2
4x1
x2
x3
3
5 =
x1 + 3x2 + 2x3
4x1 + 2x2 + x3
�
= x1
1
4
�+ x2
3
2
�+ x3
2
1
�
Thus, System I is consistent if and only if b is a nonnegative linearcombination of the columns of A. This is a cone in IRm.
Mitchell LP Duality 21 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
x =
2
40.5
0
0
3
5
Ax =
0.5
2
�(0.5,2)
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
x =
2
4t0
0
3
5
Ax =
t
4t
�
t � 0
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
x =
2
40
0
2
3
5
Ax =
4
2
�
(4,2)
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
x =
2
40
0
s
3
5
Ax =
2s
s
�
s � 0
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
x =
2
40.51.5
2
3
5
Ax =
9
7
�
(9,7)
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
Nonnegative combinations of the columns of A
b1
b2
0
4
8
4 8
System I
consistent
for b here
(1,4)
(3,2)
(2,1)
A =
1 3 2
4 2 1
�I: Ax = b, x � 0
Mitchell LP Duality 22 / 26
Visualizing the Farkas Lemma
A polar cone
System II considers the y 2 IRm satisfying AT y 0. We have:
{y 2 IRm : AT y 0} =
8<
:y 2 IR2 :
2
41 4
3 2
2 1
3
5
y1
y2
�
2
40
0
0
3
5
9=
;
=
(y 2 IR2 :
1
4
�T y1
y2
� 0,
3
2
�T y1
y2
� 0,
2
1
�T y1
y2
� 0
)
so we consider the set of vectors y that make an angle of at least ⇡/2
with each column of A. This is another cone in IRm.
If b is a nonzero vector in this cone then one solution to System II is to
take y = b, since then bT y = bT b > 0.
Mitchell LP Duality 23 / 26
f.i t :#I
Visualizing the Farkas Lemma
Picturing the polar cone
y1
y2
0
4
�4
�6
(1,4)
(3,2)
(2,1)
II: AT y 0, bT y > 0
A =
1 3 2
4 2 1
�
Mitchell LP Duality 24 / 26
Visualizing the Farkas Lemma
Picturing the polar cone
y1
y2
0
4
�4
�6
y1 + 4y2 0
(1,4)
(3,2)
(2,1)
II: AT y 0, bT y > 0
A =
1 3 2
4 2 1
�
Mitchell LP Duality 24 / 26
Visualizing the Farkas Lemma
Picturing the polar cone
y1
y2
0
4
�4
�6
2y1 + y2 0
(1,4)
(3,2)
(2,1)
II: AT y 0, bT y > 0
A =
1 3 2
4 2 1
�
Mitchell LP Duality 24 / 26
Visualizing the Farkas Lemma
Picturing the polar cone
y1
y2
0
4
�4
�6
3y1 + 2y2 0
(1,4)
(3,2)
(2,1)
II: AT y 0, bT y > 0
A =
1 3 2
4 2 1
�
Mitchell LP Duality 24 / 26
Visualizing the Farkas Lemma
Picturing the polar cone
y1
y2
0
4
�4
�6
y1 + 4y
2 = 0
2y1 +
y2=
0
{y : AT y 0}
y1 + 4y2 0
3y1 + 2y2 0
2y1 + y2 0
(1,4)
(3,2)
(2,1)
II: AT y 0, bT y > 0
A =
1 3 2
4 2 1
�
Mitchell LP Duality 24 / 26
Visualizing the Farkas Lemma
Picturing the polar cone
y1
y2
0
4
�4
�6
y1 + 4y
2 = 0
2y1 +
y2=
0
{y : AT y 0}
y1 + 4y2 0
3y1 + 2y2 0
2y1 + y2 0
System II
consistent:
take y = b
(1,4)
(3,2)
(2,1)
II: AT y 0, bT y > 0
A =
1 3 2
4 2 1
�
Mitchell LP Duality 24 / 26
IE
Visualizing the Farkas Lemma
Other choices of b
If b is in neither cone then there is a point y on the boundary of the
second cone that makes positive inner product with b. This y satisfies
the conditions of System II.
We can graph the problem in IRm. The columns of A are points in IRm,
and the two cones are subsets of IRm.
Mitchell LP Duality 25 / 26
Visualizing the Farkas Lemma
The cones
b1
b2
0
4
�4
4�6
System I
consistent
y1 + 4y
2 = 0
2y1 +
y2=
0
{y : AT y 0}
System II
consistent:
take y = b
(1,4)
(3,2)
(2,1)
Mitchell LP Duality 26 / 26
Visualizing the Farkas Lemma
The cones
b1
b2
0
4
�4
4�6
System I
consistent
y1 + 4y
2 = 0
2y1 +
y2=
0
{y : AT y 0}
System II
consistent:
take y = b
System II
consistent:
take y = (�4, 1)so bT y > 0
(-4,1)
(1,4)
(3,2)
(2,1)
Mitchell LP Duality 26 / 26
• b
¥-97.b
Visualizing the Farkas Lemma
The cones
b1
b2
0
4
�4
4�6
System I
consistent
y1 + 4y
2 = 0
2y1 +
y2=
0
{y : AT y 0}
System II
consistent:
take y = b
System II
consistent:
take y = (�4, 1)so bT y > 0
(-4,1)
System II
consistent:
take y = (1,�2)so bT y > 0
(1,-2)
(1,4)
(3,2)
(2,1)
Mitchell LP Duality 26 / 26