Abstract: The aim of this study is to present a new method for finding an optimal solution to quadratic programming problems. The principle of the method is based on calculating the value of critical point. If the critical point belongs to the set of feasible solutions, so the optimal solution to our problem is the critical point itself. If the critical point is not at in the feasible solution set, a new feasible constraint set is built by a homographic transform, in such a way that the projection of the critical point of the objective function onto this set produces the exact solution to the problem on hand. It should be noted here that the objective function may be convex or not convex. On the other hand the search for the optimal solution is to find the hyper plane separating the convex and the critical point. Notice that one does not need to transform the quadratic problem into an equivalent linear one as in the numerical methods; the method is purely analytical and avoids the usage of initial solution. An algorithm computing the optimal solution of the concave function has given.
INTRODUCTION
The methods for solving numerical optimization problems are nonlinear in essence iterative routines: a routine cannot find the exact solution in finite time. In fact the method, as treated by Delbos and Gilbert (2005), generates an infinite sequence (xt) of approximate solutions. The next iteration xt +1 is formed according to certain rules, based on local information of the problem, collected on the previous iteration which is a vector consisting of values of the objective, constraints and possibly the gradient or even higher derivatives of these functions. The optimization methods can be classified not only according to the types of problems they solve, but by type of local information they employ. From this point of view of information, the methods are divided into:
• | Routines zero-order, using only the values of the objective and constraints but not their derivatives |
• | Routines-order, using the values and gradients of the objective and constraints |
• | Routines second order, using the values, gradients and Hessians of the objective and constraints |
Optimizing methods of quadratic functions such as the ones presented by Simonnard (1973) transform the initial quadratic problem into an equivalent linear program in order and apply the simplex. Achmanov (1984) only gave approximate solutions by building convergent series which approaches the real solution. De Werra et al. (2003) studied feasible direction method with the difficult problem to choose the direction and the parameter. Hillier and Lieberman (2005) transformed the initial problem into a linear one but by adding an important number of constraints.
Bierlaire (2006) has treated a simple example needs at least ten variables. In the method presented here, without any introduction of any variable, the exact solution is reached in the first attempt.
This analytical technique determines an exact optimal solution to the problem. It has to be stressed here that the new method is general to any quadratic optimization problem. The principle of the technique is to project the critical point to a new convex built from the set of feasible solutions. the critical point x* is transformed to the point y*; then y* is projected on one hyper plan separator An algorithm for finding an optimal solution is given in this study.
SPLITTING THE OBJECTIVE FUNCTION
Consider the following problem:
(1) |
where,
By so doing a decomposition for the function f(x) holds as: f(x) = φ(x)+Ψ(x).
where, the summation runs over all the indices i for βi<0.
and
where, the sum is extended for the indices i for βi≥0.
In the present study, we study the concave function φ(x). We give, here,
the exact optimal solution by projecting the critical point
We show in theorem 1, that
Let T: Ω⊂Rn→Ω'⊂Rn be the application
that transforms the convex Ω into the convex Ω'.
OPTIMIZATION OF THE CONCAVE FUNCTION φ
Let Ω be a closed, bounded convex set of Rn. Lets put:
Theorem: There exists a closed bounded convex set Ω' of Rn and a vector y0 = (y0i)i, such that the following statements are satisfied:
Proof: For every x∈Ω let ,
Then
But
Therefore,
can be written in the following form:
Let
Thus
Because
This implies that
Because y∈Ω' then
The vector y0 is the projection of the vector y* onto the new convex Ω'.
We have,
Hence property 3.
Remark 1: If x*∈Ω, then
Remark 2: The transformation T: Ω⊂Rn→Ω'⊂Rn,
for each x∈Ω, associating
Its determinant is
Remark 3: The convex Ω is bounded. We can restrict ourselves to the case αi≥0 for each i. In fact, let's assume that we have αixi+βixi2 with αi<0.
Let
where
Therefore, it is sufficient to replace xi with
Algorithm of computing the optimal solution of the concave function φ.
Algorithm Ahmed Chikhaoui:
In the algorithm we use the supporting hyperplane. Figure 1 shows a separator supporting hyperplane H1.
The following example traits the case x*∈ Ω. Hillier and Lieberman (2005).
Example 1:
Fig. 1: | Supporting hyperplanes H1 and H2. The
supporting hyperplane H2 does not separate x* of Ω while H1 is separate
supporting hyperplane containing |
In this case β1 = 9, then Ω <> Ω'.
The critical point x* = (3, 3) ∈ Ω
The following example traits the case Ω = Ω. Example treats by Dozzi (2004).
Example 2:
In this case β1 = β2 = 1, then Ω <> Ω'.
The critical point x* = (5/2, 5/2) ∉ Ω because
where, a = (3, 1), b = 9
Here,
The following example traits the case Ω <> Ω.
Hillier and Lieberman (2005) has treated the following example. We can compare the two methods.
Example 3:
in this case β1 = 1 and β = 2, then Ω≠Ω'.
The critical point
The critical point x* = (5/2, 2) ∉ Ω because
where, a = (3, 2), b = 6.
Besides
Note that the projection of point x* onto Ω gives the point
Fig. 2: | The points: x*, y*, y0 and |
Obviously,
The point
Figure 2 shows the transformation of point x* in y*, the
projection of y* in y0 there and finally the calculation of the point
RESULTS AND DISCUSSION
After the decomposition of the objective function into two functions, one concave φ and the other convex Ψ where if the critical point belongs to omega (feasible constraint set), the optimal solution of f is the optimal solution of φ.
If the critical point does not belong to omega then the optimal solution of f is the projection of the critical point onto Ω' (new convex obtained by the translation T).
Convex Ω is replaced by a closed and lower bounded convex in Rn .
Unlike the previous techniques either analytical producing exact solution by going through linearization process or numerical producing approached solutions by Bierlaire (2006), Hillier and Lieberman (2005), Dong (2006) and De Klerk and Pasechnik (2007), the technique discussed here, produces the exact solution without going through linearization, thus it has enlarged the field of applications.