Integer Programming: Theory and Algorithmscommunity.wvu.edu › ~krsubramani › courses › sp15...

Outline

Integer Programming: Theory and Algorithms

Piotr Wojciechowski1

1LCSEEWest Virginia UniversityMorgantown, WV USA

April 11, 2015

P. Wojciechowski Optimization Methods in Finance

Outline

1 Theory of Integer Programming

Introduction

Modeling Logical Constraints

2 Solving Mixed Integer Linear Programs

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Theory of Integer ProgrammingSolving Mixed Integer Linear Programs

IntroductionModeling Logical Constraints

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Reasoning

Linear Programming allows variables to take non integer values.

In many cases rounding of solutions can be performed.

Integer Programming allows for the modeling of yes or no choices.

In these cases non-integer solutions are of little use.

Definition (Integer Program)

An Integer Program (IP), like an LP, has linear constraints and linear objectivefunction.

However, variables are now restricted to take only integer values.

Thus, an IP will have the following form.

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Reasoning

max z = c · x

A · x ≤ b

x ≥ 0

x ∈ Zn

Definition (Mixed Integer Linear Program)

In a Mixed Integer Linear Program (MILP) not all variables are restricted to onlyinteger values.

Thus both IPs and LPs are restircted forms of MILPs.

An MILP will have the following form.

max z = c · x

A · x ≤ b

x ≥ 0

xi ∈ Z, i = 1 . . . p

max z = c · x

A · x ≤ b

x ≥ 0

xi ∈ Z, i = 1 . . . p

max z = c · x

A · x ≤ b

x ≥ 0

xi ∈ Z, i = 1 . . . p

max z = c · x

A · x ≤ b

x ≥ 0

xi ∈ Z, i = 1 . . . p

max z = c · x

A · x ≤ b

x ≥ 0

xi ∈ Z, i = 1 . . . p

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Difference from Linear Programming

Unlike LPs, IPs and MILPs allow us to model a disjunction of constraints.

Example

In an IP we can model x1 ≤ 0 or −x1 ≤ −5.To do this we introduce a new (0, 1)-variable x2. The disjunction can now be expressedas:

x1 −M · x2 ≤ 0

−x1 −M · (1− x2) ≤ −5

This technique can also be used to model constraints like x1 6= 4.

Example

x1 −M · x2 ≤ 0

−x1 −M · (1− x2) ≤ −5

Example

x1 −M · x2 ≤ 0

−x1 −M · (1− x2) ≤ −5

Example

In an IP we can model x1 ≤ 0 or −x1 ≤ −5.

To do this we introduce a new (0, 1)-variable x2. The disjunction can now be expressedas:

x1 −M · x2 ≤ 0

−x1 −M · (1− x2) ≤ −5

Example

x1 −M · x2 ≤ 0

−x1 −M · (1− x2) ≤ −5

Example

x1 −M · x2 ≤ 0

−x1 −M · (1− x2) ≤ −5

Exercise

Construct an IP which models the following problem.

We wish to invest $19, 000.

Investment 1 requires an investment of $6, 700 and has a net present value of$8, 000.

Investment 2 requires $10, 000 and has a value of $11, 000.

Exercise

Solution

We get the following IP

max 8 · x1 + 11 · x2 + 6 · x3 + 4 · x4

6.7 · x1 + 10 · x2 + 5.5 · x3 + 3.4 · x4 ≤ 19

x1, x2, x3, x4 ∈ {0, 1}

Solution

max 8 · x1 + 11 · x2 + 6 · x3 + 4 · x4

6.7 · x1 + 10 · x2 + 5.5 · x3 + 3.4 · x4 ≤ 19

x1, x2, x3, x4 ∈ {0, 1}

Solution

max 8 · x1 + 11 · x2 + 6 · x3 + 4 · x4

6.7 · x1 + 10 · x2 + 5.5 · x3 + 3.4 · x4 ≤ 19

x1, x2, x3, x4 ∈ {0, 1}

Modeling Restrictions

We can also use integer programs to model restrictions on investment choices. Forexample:

We can only make two investments.

x1 + x2 + x3 + x4 ≤ 2

If we choose Investment 2, then we must also choose investment 4.

x2 − x4 ≤ 0

We cannot choose both Investment 1 and Investment 3.

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

x1 + x2 + x3 + x4 ≤ 2

x2 − x4 ≤ 0

x1 + x3 ≤ 1

LP RelaxationBranch and BoundCutting PlanesBranch and Cut

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Definition (LP Relaxation)

When given an MILP we can relax it to an LP by removing all integrality requirements.Since the LP is less constrained than the original MILP we have the following.

The optimal solution to the LP provides us with an upper bound on the solution tothe MILP.

If the LP is infeasible, then so is the MILP.

If the optimal solution to the LP has xi ∈ Z for i = 1 . . . p, then it is also the optimalsolution to the MILP.

When given an MILP we can relax it to an LP by removing all integrality requirements.

Since the LP is less constrained than the original MILP we have the following.

Example

Consider the following IP.

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

When we relax this to an LP we obtain the following as the optimal solution.

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

However when we consider the original IP we obtain the following as the optimalsolution.

(x1, x2, x3) = (2, 0, 2) z = 60

Example

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

(x1, x2, x3) = (2, 0, 2) z = 60

Example

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

(x1, x2, x3) = (2, 0, 2) z = 60

Example

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

(x1, x2, x3) = (2, 0, 2) z = 60

Example

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

(x1, x2, x3) = (2, 0, 2) z = 60

Example

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

(x1, x2, x3) = (2, 0, 2) z = 60

Example

max z = 20 · x1 + 10 · x2 + 10 · x3

2 · x1 + 20 · x2 + 4 · x3 ≤ 15

6 · x1 + 20 · x2 + 4 · x3 = 20

x1, x2, x3 ≥ 0

x1, x2, x3 ∈ Z

(x1, x2, x3) = (3.3̄, 0, 0) z = 66.6̄

(x1, x2, x3) = (2, 0, 2) z = 60

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Definition (Branch and Bound)

Branch and Bound is a technique for solving Integer Programs.

We first utilize LP Relaxation to obtain the linear optimum for the problem.

We then branch by dividing the solution space.

A branch is pruned under the following conditions.The optimum solution to the branch is integral. (Pruning by integrality)The solution space for the branch is empty. (Pruning by infeasibility)The optimum value for the branch is lower than that of a branch with an integer optimum.(Pruning by bounds)

A branch is pruned under the following conditions.

The optimum solution to the branch is integral. (Pruning by integrality)The solution space for the branch is empty. (Pruning by infeasibility)The optimum value for the branch is lower than that of a branch with an integer optimum.(Pruning by bounds)

A branch is pruned under the following conditions.The optimum solution to the branch is integral. (Pruning by integrality)

The solution space for the branch is empty. (Pruning by infeasibility)The optimum value for the branch is lower than that of a branch with an integer optimum.(Pruning by bounds)

A branch is pruned under the following conditions.The optimum solution to the branch is integral. (Pruning by integrality)The solution space for the branch is empty. (Pruning by infeasibility)

The optimum value for the branch is lower than that of a branch with an integer optimum.(Pruning by bounds)

Example

Let us consider the Integer program described by System 1.

max z = x1 + x2

−x1 + x2 ≤ 2

8 · x1 + 2 · x2 ≤ 19 (1)

x1, x2 ≥ 0

x1, x2 ∈ Z

Example

max z = x1 + x2

−x1 + x2 ≤ 2

8 · x1 + 2 · x2 ≤ 19 (1)

x1, x2 ≥ 0

x1, x2 ∈ Z

Example

max z = x1 + x2

−x1 + x2 ≤ 2

8 · x1 + 2 · x2 ≤ 19 (1)

x1, x2 ≥ 0

x1, x2 ∈ Z

Example

Let us now look at this problem graphically.

−x1 + x2 = 28 · x1 + 2 · x2 = 19

Figure: Graphical Solution

Example

−x1 + x2 = 28 · x1 + 2 · x2 = 19

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

Example

−x1 + x2 = 28 · x1 + 2 · x2 = 19

Example

−x1 + x2 = 28 · x1 + 2 · x2 = 19

Example

−x1 + x2 = 28 · x1 + 2 · x2 = 19

Example

We have that the optimal solution to System 1 is (x1, x2) = (1.5, 3.5) which givesus z = 5.

We can now branch on x1.

Any integer solution must have either x1 ≤ 1 or x1 ≥ 2.

Thus, we can create two new systems of constraints:one by adding x1 ≤ 1one by adding x1 ≥ 2

We can solve these branches graphically.

Example

Thus, we can create two new systems of constraints:

one by adding x1 ≤ 1one by adding x1 ≥ 2

Example

Thus, we can create two new systems of constraints:one by adding x1 ≤ 1

one by adding x1 ≥ 2

Example

We have that the optimal solution to System (1) is (x1, x2) = (1.5, 3.5) which gaveus z = 5.

When we add the constraint x1 ≤ 1 we get that the optimum solution is(x1, x2) = (1, 3) which gives us z = 4.

Since the optimum solution is integeral we prune this branch.

When we add the constraint x1 ≥ 2 we get that the optimum solution is(x1, x2) = (2, 1.5) which gives us z = 3.5.

Since the optimum solution lower than a known integral solution we also prune thisbranch.

All branches are now pruned and we have that the optimum integer solution is(x1, x2) = (1, 3) which gives us z = 4.

Example

We can consider the LPs solved as part of branch and bound as forming a binary tree.For System (1) this tree is:

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

Pruned by integrality

(x1, x2) = (2, 1.5)

z = 3.5

x1 ≥ 2

Pruned by bounds

Figure: Branching Tree

Example

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 3.5

x1 ≥ 2

Pruned by bounds

Example

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 3.5

x1 ≥ 2

Pruned by bounds

Example

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 3.5

x1 ≥ 2

Pruned by bounds

Example

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 3.5

x1 ≥ 2

Pruned by bounds

Exercise

Perform Branch and bound to find the solution to the following IP.

max z = 3 · x1 + x2

−x1 + x2 ≤ 2

8 · x1 + 2 · x2 ≤ 19

x1, x2 ≥ 0

x1, x2 ∈ Z

Exercise

max z = 3 · x1 + x2

−x1 + x2 ≤ 2

8 · x1 + 2 · x2 ≤ 19

x1, x2 ≥ 0

x1, x2 ∈ Z

Exercise

max z = 3 · x1 + x2

−x1 + x2 ≤ 2

8 · x1 + 2 · x2 ≤ 19

x1, x2 ≥ 0

x1, x2 ∈ Z

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

Pruned by infeasibility

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Solution

(x1, x2) = (1.5, 3.5)

(x1, x2) = (1, 3)

x1 ≤ 1

(x1, x2) = (2, 1.5)

z = 7.5

x1 ≥ 2

(x1, x2) = (2.125, 1)

z = 7.375

x2 ≤ 1

Infeasible

x2 ≥ 2

(x1, x2) = (2, 1)

x1 ≤ 2

Infeasible

x1 ≥ 3

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Definition (Cutting Planes)

When relaxing an MILP to an LP we can add additional constraints.

These added constraints are designed to be satisfied by every solution to theMILP.

However, they can still eliminate possible solutions to the relaxed LP.

Such additional constraints serve to strengthen the LP relaxation and are calledCutting Planes.

The optimal solution to the strengthened LP provides us with an upper bound on thesolution to the MILP.If the strengthened LP is infeasible, then so is the MILP.If the optimal solution to the strengthened LP has xi ∈ Z for i = 1 . . . p, then it is alsothe optimal solution to the MILP.

The optimal solution to the strengthened LP provides us with an upper bound on thesolution to the MILP.

If the strengthened LP is infeasible, then so is the MILP.If the optimal solution to the strengthened LP has xi ∈ Z for i = 1 . . . p, then it is alsothe optimal solution to the MILP.

The optimal solution to the strengthened LP provides us with an upper bound on thesolution to the MILP.If the strengthened LP is infeasible, then so is the MILP.

If the optimal solution to the strengthened LP has xi ∈ Z for i = 1 . . . p, then it is alsothe optimal solution to the MILP.

Generating Cutting Planes

Let Constraint (2) be satisfied by the solutions to an MILP with b 6∈ Z.

p∑i=1

ai · xi +n∑

ai · xi = b (2)

Let f0 = b − bbc, and let fi = ai − baic for i = 1 . . . p.We can now rewrite Constraint (2) as

∑i≤p,fi≤f0

fi · xi +∑

i≤p,fi>f0

(fi − 1) · xi +n∑

ai = k + f0.

Where k ∈ Z.

p∑i=1

ai · xi +n∑

ai · xi = b (2)

∑i≤p,fi≤f0

fi · xi +∑

i≤p,fi>f0

(fi − 1) · xi +n∑

ai = k + f0.

Where k ∈ Z.

p∑i=1

ai · xi +n∑

ai · xi = b (2)

∑i≤p,fi≤f0

fi · xi +∑

i≤p,fi>f0

(fi − 1) · xi +n∑

ai = k + f0.

Where k ∈ Z.

p∑i=1

ai · xi +n∑

ai · xi = b (2)

Let f0 = b − bbc, and let fi = ai − baic for i = 1 . . . p.

We can now rewrite Constraint (2) as

∑i≤p,fi≤f0

fi · xi +∑

i≤p,fi>f0

(fi − 1) · xi +n∑

ai = k + f0.

Where k ∈ Z.

p∑i=1

ai · xi +n∑

ai · xi = b (2)

∑i≤p,fi≤f0

fi · xi +∑

i≤p,fi>f0

(fi − 1) · xi +n∑

ai = k + f0.

Where k ∈ Z.

If k ≥ 0 then Constraint (2) implies that

∑i≤p,fi≤f0

fif0· xi −

∑i≤p,fi>f0

1− fif0· xi +

n∑i=p+1

f0≥ 1.

If k ≤ −1 then Constraint (2) implies that

−∑

i≤p,fi≤f0

fi1− f0

· xi +∑

i≤p,fi>f0

1− fi1− f0

· xi −n∑

1− f0≥ 1.

Since each xi is non negative we will always have that.

∑i≤p,fi≤f0

fif0· xi +

∑i≤p,fi>f0

1− fi1− f0

· xi +∑

i>p,ai>0

∑i>p,ai<0

1− f0≥ 1.

This constraint is known as the Gomory mixed integer cut.

∑i≤p,fi≤f0

fif0· xi −

∑i≤p,fi>f0

1− fif0· xi +

n∑i=p+1

f0≥ 1.

−∑

i≤p,fi≤f0

fi1− f0

· xi +∑

i≤p,fi>f0

1− fi1− f0

· xi −n∑

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi +

∑i≤p,fi>f0

1− fi1− f0

· xi +∑

i>p,ai>0

∑i>p,ai<0

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi −

∑i≤p,fi>f0

1− fif0· xi +

n∑i=p+1

f0≥ 1.

−∑

i≤p,fi≤f0

fi1− f0

· xi +∑

i≤p,fi>f0

1− fi1− f0

· xi −n∑

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi +

∑i≤p,fi>f0

1− fi1− f0

· xi +∑

i>p,ai>0

∑i>p,ai<0

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi −

∑i≤p,fi>f0

1− fif0· xi +

n∑i=p+1

f0≥ 1.

−∑

i≤p,fi≤f0

fi1− f0

· xi +∑

i≤p,fi>f0

1− fi1− f0

· xi −n∑

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi +

∑i≤p,fi>f0

1− fi1− f0

· xi +∑

i>p,ai>0

∑i>p,ai<0

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi −

∑i≤p,fi>f0

1− fif0· xi +

n∑i=p+1

f0≥ 1.

−∑

i≤p,fi≤f0

fi1− f0

· xi +∑

i≤p,fi>f0

1− fi1− f0

· xi −n∑

1− f0≥ 1.

∑i≤p,fi≤f0

fif0· xi +

∑i≤p,fi>f0

1− fi1− f0

· xi +∑

i>p,ai>0

∑i>p,ai<0

1− f0≥ 1.

Example

Let us return to System (1).Adding slack and surplus variables yields the system

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

Using the simplex method we get the following at the linear optimum,

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

We can now use the constraint x2 + 0.8 · x3 + 0.1 · x4 = 3.5 to generate a Gomorymixed integer cut.

Example

Let us return to System (1).

Adding slack and surplus variables yields the system

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

Example

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

Example

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

Example

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

Example

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

Example

max z = x1 + x2

−x1 + x2 + x3 = 2

8 · x1 + 2 · x2 + x4 = 19

x1, x2, x3, x4 ≥ 0

x1, x2, x3, x4 ∈ Z

z = 5− 0.6 · x3 − 0.2 · x4

x1 = 1.5 + 0.2 · x3 − 0.1 · x4

x2 = 3.5− 0.8 · x3 − 0.1 · x4

Example

Use the constraint x2 + 0.8 · x3 + 0.1 · x4 = 3.5 to generate a Gomory mixed integercut we get the constraint

1− 0.81− 0.5

· x3 +0.10.5· x4 ≥ 1.

This is equivalent to the constraint

2 · x3 + x4 ≥ 5.

Since x3 = 2 + x1 − x2 and x4 = 19− 8 · x1 − 2 · x2 this constraint is equivalent to

3 · x1 + 2 · x2 ≥ 9.

Example

1− 0.81− 0.5

· x3 +0.10.5· x4 ≥ 1.

2 · x3 + x4 ≥ 5.

3 · x1 + 2 · x2 ≥ 9.

Example

1− 0.81− 0.5

· x3 +0.10.5· x4 ≥ 1.

2 · x3 + x4 ≥ 5.

3 · x1 + 2 · x2 ≥ 9.

Example

1− 0.81− 0.5

· x3 +0.10.5· x4 ≥ 1.

2 · x3 + x4 ≥ 5.

3 · x1 + 2 · x2 ≥ 9.

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Example

−x1 + x2 = 2

8 · x1 + 2 · x2 = 19

3 · x1 + 2 · x2 = 9

Outline

Introduction

LP Relaxation

Branch and Bound

Cutting Planes

Branch and Cut

Definition (Branch and Cut)

Branch an Cut is a combination of branch and bound and cutting planes.

This method proceeds just like regular branch and bound except that at eachbranch the LP is strengthened using cutting planes.

Integer Programming: Theory and Algorithmscommunity.wvu.edu › ~krsubramani › courses › sp15...

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