Week 7 - Exact Solving Techniques

Solution Techniques

Exact Optimization Methods

Exact Optimization Methods

We will consider exact optimization methods for solving transport

optimization problems in this part of our course.

What are the advantages and disadvantages of exact

optimization methods?

Branch and Cut

Branch and Price

Travelling Salesman Problem

Vehicle Routing Problem and Variants

...

Linear Program – Definition

Linear Program

Introduction to the Simplex Algorithm

The simplex algorithm has been developed in 1947 by George

Dantzig.

Today it is possible to solve linear programs involving several

millions of variables and constraints.

There exist worst-case examples that can not be solved by the

simplex algorithms in polynomial time.

However, the simplex algorithm is the most commonly used in

practice.

There exist polynomial time approaches for solving linear

programs, e.g.: the ellipsoid method.


− x1 + 3x2 = 12

x1 + x2 = 8

2x1 − x2 = 10

0

3

4

5

6

0 1 4 2 3 5 6

2

1

3x1 + 2x2 = 22


More details: Algorithmics, Mathematical Programming

R. Vanderbei.

Linear Programming: Foundations and Extensions. Kluwer. 1998.

Integer Program

Integer Program

Total Enumeration

• Class exercise- C1

• Exponential growth!!

LP Relaxation

Relaxation (omitting) of the integrality constraints

Linear programs can be solved efficiently

Lower bound for integer programs

Solution values of the relaxation can hint at solutions of the

integer problem

Branch and Bound

Enumeration methods: Search tree is generated by depth-

first search

Computing lower and upper bounds in order to cut off parts of

the search tree.

Lower bound: LP relaxation

Upper bound: feasible solution

Branch and Bound

Solution of the LP relaxation in the root node

Let xi be a variable with a fraction value xi .

The search tree is then branched as follows:

and

Branch and Bound for Integer Optimization

Example – Branch-and-Bound

We consider the following integer program:

max −7x1 − 3x2 − 4x3

x1 + 2x2 + 3x3 − x4 = 8 3x1 + x2 + x3 − x5 = 5

x1, x2, x3, x4, x5 ≥ 0

x1, x2, x3, x4, x5 ∈ Z



The LP relaxation provides the solution:

2 19 x3 = x4 = x5 = 0 , x1 =

5 , x2 =

5

∗ 71 5

with objective value c = − (= −14, 2). We obtain the upper bound −15.


P0

U = −∞ c∗ = −15



Branch on variable x2:

P1 = P0 ∩ {x | x2 ≤ 3}

P2 = P0 ∩ {x | x2 ≥ 4}

P1: Subsequent problem.

An optimal solution for the LP relaxation LP1 is

1 1 x4 = x5 = 0 , x1 =

2 , x2 = 2 , x3 =

2

∗ 29 2

and c = − (with upper bound -15).


P0

U = −∞ c∗ = −15

x2 ≥ 4 x2 ≤ 3

P1

U = −∞ c∗ = −15 P 2



P1 has to be further decomposed:

P3 = P1 ∩ {x | x1 ≤ 0}

P4 = P1 ∩ {x | x1 ≥ 1}

The active problems are: K = {P2, P3, P4}.

Solving LP3 gives

x1 = x5 = 0 , x2 = 3 , x3 = 2 , x4 = 4

and c∗ = −17.

P3 is solved ⇒ the currently best solution has value −17.


P4 P3

P0

U = −17

U = −∞ c∗ = −15

x2 ≥ 4

P2

x2 ≤ 3

P1

x1 ≥ 1 x1 ≤ 0

U = −∞ c∗ = −15



Solving LP4 gives:

1 4 x4 = 0 , x1 = 1 , x2 = 3 , x3 =

3 , x5 =

3

∗ 52 1 3 3

and c = − = −17 .

The upper bound (−18) is worse than the best solution, thereby P4 is

solved.


P4 P3

P0

U = −17

U = −17 c∗ = −18

U = −∞ c∗ = −15

x2 ≥ 4

P2

x2 ≤ 3

P1

x1 ≥ 1 x1 ≤ 0

U = −∞ c∗ = −15


• Example – Branch-and-Bound

• Solving P2 gives:

∗ 43 3

and c = − .

P2 is not yet solved, we have to branch:

P5 = P2 ∩ {x | x1 ≤ 0}

P6 = P2 ∩ {x | x1 ≥ 1}


P4 P3

P0

P5 P6

U = −17 c∗ = −15

U = −17 c∗ = −18

x2 ≤ 3

P1

U = −∞ c∗ = −15

x2 ≥ 4

P2

x1 ≤ 0

x1 ≥ 1 x1 ≤ 0 x1 ≥ 1

U = −∞ c∗ = −15



Solving LP5 gives

x1 = x3 = x5 = 0 , x2 = 5 , x4 = 2

and c∗ = −15.

This is a new best solution with value −15. P5 is thereby solved.

P6 does not need to be considered anymore, since LP0 shows that no solution better than −15 is possible.


P4 P3

P0

P5 P6

U = −17 c∗ = −15

U = −15 c∗ = −15

U = −17 c∗ = −18

x2 ≤ 3

P1

U = −∞ c∗ = −15

x2 ≥ 4

P2

x1 ≤ 0

x1 ≥ 1 x1 ≤ 0 x1 ≥ 1

U = −∞ c∗ = −15

Branch and Bound

Multiple variants:

Node selection

Variable selection

Branching decisions

Possible problems: long run-times, large memory requirements

More details: Algorithmics, Mathematical Programming

L. Wolsey. Integer Programming. Wiley. 1998.

Relaxations

1. Constraint relaxations: New feasible solutions may be allowed but none should be lost!

C2-Exercises! Are the following valid constraint relaxations?

2. Continous relaxations (LP relaxations )

Relaxations

• If an optimal soltion to a relaxation is also feasible in the model it relaxes, the solution is optimal in that original model.

• C3:Exercises!

Compute by inspection optimal solution to each of the following relaxations and determine whether we can conclude that the relaxation optimum is optimal in the original model.

Relaxations • If an optimal solution to a relaxation is also feasible in the model it relaxes, the solution is

optimal in that original model.

• More commonly, things are not that simple BUT: 1) We have the bound value (optimal value of any

relaxation of a maximize model yields an upper bound on the optimal value of the full model . The optimal value of any relaxation of a minimization model yields a lower bound.)

2) Maybe a good starting point for constructing a good heuristic solution.

3) Proving infeasability: If a constraint relaxation infeasible so is the full model it relaxes.

Rounded solutions from relaxations

• Many relaxations produce optimal solutions that are easily rounded to good feasible solutions.

• C4: Exercises: Round the LP relaxation optimum to an approximate solution for the original model. State the best lower and upper bounds on the optimal integer solution.

Stronger LP relaxations

HOW?

1. Different formulations of the same problem

Example : E1

2. Choosing smallest Big-M’s.

C5:Exercise

3. Adding new valid inequality constraints.

Valid inequalities

• A linear inequality is a valid inequality if it holds for all integer feasible solutions to the model.

• To strengthen the solution it must cut off some feasible solutions to the current LP relaxation that are not feasible to the full ILP model.

• Also called cutting planes

• Class exercise : C6

Cutting Plane Method

Exponentially many constraints! In mathematical optimization, the cutting-plane method is an umbrella term for optimization methods which iteratively refine a feasible set or objective function by means of linear inequalities, termed cuts.

Cutting Plane Method – Basic Idea

-Start with a small subset of constraints and solve the resulting LP

-Check whether the obtained solution is feasible for all constraints

-If “yes”: optimal soloution found

-Else find a violated constraint and add it to the LP

-Iterate until no more constraints need to be added

171 / 319

https://en.wikipedia.org/wiki/Mathematics

https://en.wikipedia.org/wiki/Optimization_(mathematics)

https://en.wikipedia.org/wiki/Feasible_set

Cutting Plane Method

Branch-and-Cut

Truncated cutting plane method returns a solution for an LP

relaxation

Branch-and-bound is combined with the cutting plane method:

Branch-and-Cut

The subproblems are solved using a truncated cutting plane

method

If no further cuts are found and the solution is not integer

feasible, branching is performed

Every new subproblem is again solved using the truncated

cutting plane method

Branch-and-Cut

Compared to classical branch-and-bound, the search tree in a

branch-and-cut apporach is usually significantly smaller.

The success of branch-and-cut approaches relies on:

1.the use of strong LP relaxations

2.fast separation algorithms (generating cutting planes)

3.a multitude of algorithmic tricks

VRPTW - Bounds

Lower Bounds

• The network lower bound can be obtained by removing the

capacity and time window constraints.

• LP relaxation • Better bounds can be obtained using mode complex algorithms

such as column generation.

Upper bounds

• Route construction

• Route improvement

• Metaheuristics

Column Generation

The basic idea of column generation originates in the simplex

algorithm.

Only variables with negative reduced costs are entering the

basis.

It is sufficient to start with a small number of columns.

As long as variables with negative reduced cost can be

determined, they are added to the problem which is

subsequently solved again.

Column Generation

Subproblem: Search variables with negative reduced cost

(Pricing-Problem).

If a such a variable/column has be found it can be added to the

master problem, which is re-solved.

This process is repeated as long as new columns with negative

reduced cost are found, if no such column existis an optimal LP

solution has been determined.

Reduced Cost

The reduced cost are the cost of a variable in the current simplex

tableau.

Only variables with negative reduced cost can therefore improve

the objective value.

The reduced cost of a variable can be computed from the values

of the dual variables.

Given a linear program {min cx | Ax ≤ b, x ≥ 0}

Then the reduced cost are c − yA, with y the dual variables of

the linear program.

More details can be found in: R. Vanderbei. Linear Programming:

Foundations and Extensions. Kluwer. 1998.

Branch-and-Price

LPs with exponentially many variables can be solved. This

does not give us a solution for the integer problem.

An optimal solution for integer problems consists of a

combination of branch and bound and column generation:

Branch-and-Price

Branch-and-Price

Columns are generated until an optimal LP solution is reached.

If this solution is not integral, the problem is divided (branching).

For each subproblem columns are generated again.

This process is repeated until an optimal solution is reached.

Lagrangian Relaxation

Lagrangian relaxation is a technique well suited for problems where the constraints can be divided into two sets: • “good” constraints, with which the problem is solvable very easily • “bad” constraints that make it very hard to solve. The main idea is to relax the problem by removing the “bad” constraints and putting them into the objective function, assigned with weights (the Lagrangian multiplier). Each weight represents a penalty which is added to a solution that does not satisfy the particular constraint.


We assume that optimizing over the set X can be done very easily, whereas adding the “bad” constraints Ax ≥ b makes the problem intractable.


• Therefore, we introduce a dual variable for every constraint of Ax ≥ b. The vector λ ≥ 0 is the vector of dual variables (the Lagrangian multipliers) that has the same dimension as vector b. For a fixed λ ≥ 0, consider the relaxed problem


• By assumption, we can efficiently compute the optimal value for the relaxed problem with a fixed vector λ.

• Lemma (Weak duality). Z(λ) provides a lower bound on




Solving the lagrangian dual

Mostly used Techniques: -Subgradient optimization method -Multiplier adjustment.

Solving the lagrangian dual Sign restrictions on multipliers: -If the relaxed constraint has form ≥ Multiplier is ≤0 for a maximization model ≥0 for a minimization model -If the relaxed constraint has form ≤ Multiplier is ≥ 0 for a maximization model ≤ 0 for a minimization model -Class Exercise C7

Wrap-up

• Suppose that we have some problem instance of a combinatorial optimisation problem and further suppose that it is a minimisation problem.

• We draw a vertical line representing value (the higher up this line the higher the value) then somewhere on this line is the optimal solution to the problem we are considering.

Wrap-up

• Exactly where on this line this optimal solution

lies we do not know, but it must be somewhere! • Conceptually therefore this optimal solution value

divides our value line into two: • above the optimal solution value are upper

bounds, values which are above the (unknown) optimal solution value

• below the optimal solution value are lower bounds, values which are below the (unknown) optimal solution value.

Wrap-up

• In order to discover the optimal solution value then

any algorithm that we develop must address both these issues i.e. it must concern itself both with upper bounds and with lower bounds.

• In particular the quality of these bounds is important to the computational success of any algorithm:

• we like upper bounds that are as close as possible to the optimal solution, i.e. as small as possible

• we like lower bounds that are as close as possible to the optimal solution, i.e. as large as possible.

Wrap-up Upper bounds Typically upper bounds are found by searching for feasible

solutions to the problem, that is solutions which satisfy the constraints of the problem.

A number of well-known general techniques are available to find feasible solutions to combinatorial optimisation problems, for example:

• interchange

• metaheuristics:

• tabu search

• simulated annealing

• variable neighbourhood search

• genetic algorithms (population heuristics).

In addition, for any particular problem, we may well have techniques which are specific to the problem being solved.

Wrap-up Lower bounds

• One well-known general technique which is available to find lower bounds is linear programming relaxation. In linear programming (LP) relaxation we take an integer (or mixed-integer) programming formulation of the problem and relax the integrality requirement on the variables.

• This gives a linear program which can be solved optimally using a standard algorithm (simplex or interior point)

• The solution value obtained for this linear program gives a lower bound on the optimal solution to the original problem.

• Another well-known (and well-used) technique which is available to find lower bounds is lagrangean relaxation.

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Week 7 - Exact Solving Techniques