Futuras investigaciones

Distributed optimization is itself a very broad term. At the beginning, it can be said half-way that a complicated optimization problem can be divided into simpler

tasks using the decomposition methods (as in the case of Multiple Shooting meth- ods), and then solved separately. However, this division can not be accidental and must be supplemented by a number of equality constraints that maintain the in- tegrity of the original task. Generally speaking, if the distribution of the original problem is well-designed, distributed problem solution is generally simpler and has better convergence to the optimum than the solution of the original, central- ized task.

For a full understanding of the decomposition techniques, the basic terms and methods of decomposition as a Separable Problem, Complicating Variables, Pri- mal and Dual decomposition, and after that the distributed identification algo- rithm work flow will be introduced. The following text is based on prof. Stephen Boyd’s lessons, for more see [12] and [11].

In this section, symbols x, y and functions f, g have all different meaning than have been previously used.

2.5.1 Separable Problem and Complicating Variables A simple optimization problem

min

x1,x2 {f1(x1) + f2(x2)} ,

subj. to x1∈ C1, x2 ∈ C2, (2.20) can be solved for x1and x2separately, either sequentially or in parallel, because solutions to these two problems are not tied together. Variables x1and x2are called private or local variables.

But if the problem is changed a little

min

x1,x2,y {f1(x1, y) + f2(x2, y)} , (2.21)

we get the problem coupled together just by using a variable y. The variable y is here called complicating or coupling variable. In essence, this simple problem is basi- cally the same as the problem solved by the distributed identification algorithm presented in this thesis.

The distribution of our task lies in the solution of many steady-state points in which the real system was measured. These so-called operating points form sepa- rable problems and can be solved separately with the given global vector of param- eters.

A way to solve this problem is generally by a distributed iterative algorithm. Firstly, the complicating variable y is fixed, then the separable problems x1, . . . , xn

are solved according to a given criterion. Based on how well the solution suits or fails in sum, the global identifier decides how to update the complicating variable for the next iteration.

2.5.2 Primal and Dual Decomposition

Two fundamental techniques of decomposition of this problem show, how to prop- erly fix and work with the complicating variable from the example (2.21). These are the so called Primal and Dual decompositions.

Primal Decomposition

For a fixed complicating variable y the original problem (2.21) can be decomposed into two subproblems

subproblem 1 : min

x1 f1(x1, y),

subproblem 2 : min

x2 f2(x2, y), (2.22)

where optimal values of these subproblems are ϕ₁(y) and ϕ₂(y). Then the original problem (2.21) is equivalent to the problem

min

y

{

ϕ₁(y) + ϕ₂(y)}, (2.23)

which is called the master problem. The solution to this problem has already been mentioned in this section – for the fixed complicating variable y, in general the sub- problems 1, . . . , n will be solved, while the subproblems must also tell the opti- mizer of the complicating variable in some way how the selected y value matches

them. For example, when using to solve a problem a gradient algorithm, the gra- dient g1 ∈ ∇ϕ₁(y), . . . , gn ∈ ∇ϕ_n(y) can be computed and after all subproblems

are solved, the complicating variable can be updated in the direction of the sum of the gradients y := y− αk

∑

jgjat the selected step of the αkmethod. Graph-

ical representation of Primal decomposition can be seen in Figure 2.5.1. Primal decomposition is called Primal because there the original, primal variable is dis- tributed.

The primary benefit of Primal decomposition is working directly with a global variable. If the run of the distributed algorithm is interrupted at any time, it is always possible to determine the value of the global variable, which is also used in the task convergence monitoring. A major disadvantage of this decomposition is the case where constraints are attached to the problem that binds both the local and the global variables when the distribution of these constraints may not be easy, i.e.

s1(x1, y) ≤ 0, ...

sn(xn, y) ≤ 0. (2.24)

Here, different constraints are used in various sub-problems. It may happen that the solver, solving the original problem, sends within the i-th iteration y, such that it will not meet all the (2.24). This problem does not arise in Dual decomposition.

Dual Decomposition

In the original problem (2.21) the complicating variable y is now split into two new variables y1and y2, and the problem is rewritten as:

min

x1,x2,y1,y2, {f1(x1, y1) + f2(x2, y2)} ,

where y1and y2are local versions of complicating variable y and y1 = y2is a con- sensus or consistency constraint.

As can be seen, two separable problems are created, which can be solved sepa- rately. But it must not be forgotten that the equality constraints must be incorpo- rated into these subproblems. The lagrangian of this problem is

L(x1, y1, x2, y2) = f1(x1, y1) + f2(x2, y2) + λT(y1− y2), (2.26) which is separable and these subproblems can be solved separately. λ is a Lagrange multiplier, and it has the meaning of price for violating the constrain y1− y2= 0. Therefore, the individual subproblems are

subproblem 1 : g1(λ) = min x1,y1 { f1(x1, y1) + λTy1 } , subproblem 2 : g2(λ) = min x2,y2 { f2(x2, y2)− λTy2 } , _(2.27)

with the master problem

g(λ) = max

λ {g1(λ) + g2(λ)} . (2.28)

The solution to this problem proceeds in a similar way as in the case of Primal decomposition. The optimizer optimizing the λ determines the price for violating a constraint, and the subproblems k = 1, . . . , K are then solved. Then the price for a violation of the constraint is changed based on the requirements of individ- ual subproblems, for example λ := λ − αk(y2 − y1), and the whole algorithm

runs again. At the beginning of algorithm run, λ is usually low and increases dur- ing the iteration progresses. Gradually, the local variables y1, . . . , ynapproach an

optimal value. The advantage of this approach is that at any chosen price λ so- lution, subproblems always have solutions. Graphical representation of the Dual decomposition can be seen in Figure 2.5.1.

A major disadvantage of Dual decomposition is the fact, that during the run of the algorithm we do not have a value of original complicating variable, because each subproblem can send different value of y1, . . . , ynwithin the optimal solution

is the fact, that if the original problem contains a constraint that combines local variables with complicating variables, these constraints can be pushed to the sub- problem.

Figure 2.5.1: Graphical representation of Primal and Dual decompositions.