Abstract and Applied Analysis

Volume 2014 (2014), Article ID 163263, 12 pages

http://dx.doi.org/10.1155/2014/163263

Research Article

A Global Optimization Algorithm for Signomial Geometric Programming Problem

Xue-Ping Hou, Pei-Ping Shen, and Yong-Qiang Chen

College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China

Received 17 December 2013; Accepted 16 January 2014; Published 30 March 2014

Academic Editor: Yisheng Song

Copyright © 2014 Xue-Ping Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper presents a global optimization algorithm for solving the signomial geometric programming (SGP) problem. In the algorithm, by the straight forward algebraic manipulation of terms and by utilizing a transformation of variables, the initial nonconvex programming problem (SGP) is first converted into an equivalent monotonic optimization problem and then is reduced to a sequence of linear programming problems, based on the linearizing technique. To improve the computational efficiency of the algorithm, two range reduction operations are combined in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (SGP) by means of the subsequent solutions of a series of relaxation linear programming problems. And finally, the numerical results are reported to vindicate the feasibility and effectiveness of the proposed method.

1. Introduction

The signomial geometric programming (SGP) problem can be formulated as the following nonlinear optimization problem:

where

are positive integers and and are all arbitrary real constant coefficients and exponents, respectively. In general, the problem (SGP) corresponds to a nonlinear optimization problem with nonconvex objective function and constraint set. As noted by [1, 2], many nonlinear programming problems may be restated as geometric programming with little additional effort by simple techniques such as change of variables or by straightforward algebraic manipulation of terms. Additionally, (SGP) problem has found a wide range of applications in production planning, location, distribution contexts in risk management problems, various chemical process design and engineering design situations, and so on [3–10]. Hence, it is necessary to present good algorithms for solving (SGP).

The theory of (SGP) was initially developed over three decades ago by Duffin et al. [11–13]. Subsequently, it had been studied by a number of researchers. In general, local optimization approaches for solving (SGP) problem include three kinds of methods as follows. First, successive approximation by posynomials has received the most popularity [14]. Second, Passy and Wilde [15] developed a weaker type of duality to accommodate this class of nonlinear optimization. Third, general nonlinear programming methods [16]. Though local optimization methods for solving SGP problem are ubiquitous, the global optimization algorithm based on the characteristics of (SGP) problem is scarce. When in is positive integer or rational number, some authors in [8, 17–19] developed the corresponding global solution methods for (SGP). In this case that each is real, Maranas et al. [20] proposed a global optimization branch and bound algorithm, by using the exponential variable transformation of (SGP) and the convex relaxation. Shen and Zhang [21] also proposed a global optimization algorithm based on the exponential variable transformation of (SGP) and the linear relaxation. Recently, Shen et al. [22] presented a robust algorithm for (SGP) problem by seeking an essential optimal solution. Wang et al. [23] developed a general algorithm for solving (SGP) problem with nonpositive degree of difficulty. Qu et al. [24] proposed a global optimization algorithm using linear relaxation for (SGP) problem.

In this paper we present a new global optimization algorithm for (SGP) problem by using several reduction operations and by solving a sequence of linear programming problems over partitioned subsets. The proposed method uses a convenient transformation based on the characteristics of (SGP) problem; thus, the original problem (SGP) is equivalently reformulated as a monotonic optimization problem (), that is, the objective function is increasing and all the constrained functions can be denoted by the difference of two increasing functions in problem (). A comparison of this method with other methods reviewed above is given below. First, the proposed linear relaxation is based on the monotonic optimization problem (), which applies more information of the functions of (SGP). And what is more important is that the proposed reduction operations which are adopted in our global optimization algorithm can cut away a large part of the region in which the global optimal solution of (SGP) does not exist. This solution procedure will be more efficient than the methods in [21, 25, 26]. Second, the problem investigated in this paper generalizes those of [8, 17–19]. Furthermore, our method is more convenient in computation than the convex relaxation [19] because the main work is to solve the linear programs and the zeros of strictly monotonic functions of one variable over the interval [0,1), which can be solved very efficiently by the existing methods, for example, by the simplex method and the bisection search method. Third, numerical results and comparison with other methods are conducted to show the potential advantage of the proposed algorithm.

The remainder of this paper is organized as follows. The next section converts the (SGP) problem into a monotonic optimization problem. We discuss the rectangular branching operation, the lower bounding operation, and the reducing operations needed in our algorithm in Section 3. Section 4 incorporates this approach into an algorithm for solving (SGP) and shows the convergence property of the algorithm. In Section 5, we report the results of solving some numerical examples with the algorithm. A summary is presented in the last section.

2. Equivalent Problem

In order to convert (SGP) problem into an equivalent optimization problem (), for each , , let us denote

By multiplying both sides of each constraint inequality of (SGP) with and by applying the exponent transformation

to the formulation (SGP), we can obtain the following equivalent problem:

Next, for convenience, for each , we assume, without loss of generality, that for and for , and some notation is introduced as follows:

Thus, by using , , let us calculate

Then, by introducing some additional variables , , with , we can convert the problem (SGP1) into

where

Additionally, for the sake of simplicity, let ; the problem () can be rewritten as the following form:

where

Note that each function of problem () is increasing (i.e., a function is said to be increasing if for all satisfying , ). Thus problem () is a monotonic optimization problem, and the key equivalent result for problems (SGP) and () is given by Theorem 1.

Theorem 1. is a global optimal solution for problem (SGP) if and only if is a global optimal solution for problem (P), where

Proof. The proof of this theorem follows easily from the definitions of problems (SGP) and (); therefore, it is omitted here.

From Theorem 1, notice that, in order to solve problem (SGP), we may solve problem () instead. In addition, it is easy to see that the global optimal values of problems (SGP) and () are equal. Based on the above discussion, here, from now on we assume that the original problem (SGP) has been converted into the problem (); then a general approach will be considered for solving problem ().

3. Key Algorithm Processes

To globally solve the problem (), a branch-reduce-bound (BRB) algorithm will be proposed. This algorithm proceeds according to the standard branch and bound scheme with three key processes: branching, reducing, and bounding.

The branching process consists in a successive rectangular partition of the initial box following in an exhaustive subdivision rule, that is, such that any infinite nested sequence of partition sets generated through the algorithm shrinks to a singleton. A commonly used exhaustive subdivision rule is the standard bisection.

The reducing process consists in applying reduction operations to reduce the size of the current partition set . The process aims at tightening the box containing the feasible portion currently still of interest.

The bounding process consists in using the linearization method to give a better lower bound.

Next, we begin to establish the approaches processes.

3.1. Lower Bound

At a given stage of the BRB algorithm for (), let be a rectangle during the partitioning procedure and still of interest; we intend to compute a lower bound of the optimal value of () over . Restrict the problem () to :

Denote the optimal objective function value of problem by .

Since is increasing, an obvious bound is ; although very simple, this bound suffices to ensure convergence of the algorithm. However, the following procedure may give a better bound.

Our main method for computing a lower bound of over is to solve the relaxation linear programming of . The linear relaxation of the problem can be realized by underestimating every function and and by overestimating every function , for each . All the details for generating the linear relaxation will be given in the following.

Denote

where . In addition, let

where , .

Theorem 2. Consider the functions , , and , for any , where and . Then the following two statements are valid.

(i)The function is the concave envelope of the function over , and the function is a supporting hyperplane of , which is parallel with . Moreover, the functions , , and satisfy

(ii)The differences and satisfy , where

Proof. The proof is similar to Theorem 1 in [21]; therefore, it is omitted here.

Remark 3. From Theorem 2, we can follow that the functions and enough approximate the function as , respectively.

From Theorem 2, it is obvious that for all we have

where .

Consequently, we obtain the following linear programming as a linear relaxation of over the partition set :

An important property of is that its optimal value satisfies

and thus, from (21), the optimal value of provides a valid lower bound for the optimal value of over .

Based on the above discussion, for any rectangle , in order to obtain a lower bound of the optimal value of the problem , we may compute such that

Clearly, defined in (22) satisfies

and is consistent. It can provide a valid lower bound and guarantee convergence.

3.2. Reduction Operations

Clearly, the smaller the rectangle is, the tighter the lower bound of will be, and therefore the closer the feasible solution of will be to the optimal solution of . To show this, the next results give two reduction operations (i.e., reduction rules A and B) to reduce the size of this partitioned rectangle without losing any feasible solution currently still of interest.

3.2.1. Reduction Rule A

Rule A is based on the monotonic structure of the problem . At a given stage of the BRB algorithm for , for a rectangle generated during the partitioning procedure and still of interest, let be the object function value of the best so far feasible solution to problem . Given an , we want to find a feasible solution of such that or else establish that no such exists. So the search for such can then be restricted to the set , where

The reduction rule aims at replacing the rectangle with a smaller rectangle without losing any point , that is, such that . The rectangle satisfying this condition is denoted by with

To illustrate how is deduced by this rule, we first define the following functions.

Definition 4. Given two boxes and with , for , , the functions , , and are defined by

where denotes the th unit vector of , that is, a vector such that , , and the functions , , and are given in problem (), respectively.

Clearly, the functions , , and are either constant or strictly monotonic over the interval [0,1) from the properties of , , and . By using these functions, can be given as follows.

Theorem 5. If or for some , then .

If and for each , then , where

are given by

Proof. (i) By the increasing property of , , and , if , then for every . If there exists such that , then for every . In both cases, .

(ii) Given any point satisfying

we will show that . Let

Firstly, we will show that . If , then there exists index such that

We consider the following two cases.

Case  1. If , then from (31) we have , conflicting with ; that is, .

Case  2. If , the function must be strictly decreasing in single variable over the interval [0,1). If the function is not strictly decreasing in single variable , we get must be a constant over the interval [0,1). In this case, we have

It follows from the definition of that , contradicting with .

Since the function is strictly decreasing, it follows from (31) and the definition of that

hence,

In addition, since is an increasing function in -dimension variable and , we have

conflicting with .

Based on the above discussion, we have ; that is, in either case.

Secondly, we also can show from that

Supposed that , then there exists some such that

that is, there exists such that

By the definition of , there are the following two cases to consider.

Case  1. If , then from (38) we have , conflicting with ; that is, .

Case  2. If , the function is strictly increasing in single variable . If the function is not strictly increasing in single variable , we get must be a constant over the interval [0,1). In this case, we have

or

It follows from the definition of that , which is a contradiction with .

Since the function is strictly increasing, from (31) and the definition of , it implies that

or

Assume that (41) holds; we can derive from (38) that

It follows from and increasing that

conflicting with .

If (42) holds, we obtain from (38) that

since and is increasing, we have

It is a contradiction with .

From the above results, we must have in both cases, and this ends the proof.

Remark 6. Clearly, for any , and defined in Theorem 5 must exist and be unique, since the functions , , and are all continuous and increasing.

3.2.2. Reduction Rule B

For any with , without loss of generality, we assume the above relaxation linear problem can be rewritten as

Let

where , .

Theorem 7. For any rectangle , if , then there exists no optimal solution of over ; otherwise, consider the following two cases: if there exists some satisfying and , then there is no optimal solution of over ; conversely, if and for some , then there does not exist optimal solution of over , where

Theorem 8. For any rectangle , if for some , then there exists no feasible solution of problem over , otherwise, consider the following two cases: if there exists some index and satisfying and , then there is no feasible solution of the problem over ; conversely, if and for some and , then there exists no feasible solution of the problem over , where

Proof. The proof of the Theorems 7 and 8 is similar to Theorems 2 and 3 in [27], respectively; therefore, it is omitted here.

By Theorems 7 and 8, we can give a new reduction rule B to reject some regions in which the globally optimal solution of does not exist. The computation procedure of this rule is summarized as follows.

Step 1. Compute in (48). If , let ; otherwise, compute in (49). If and for some , then let and with . If and for some , then let and with .

Step 2. For any , compute in (48). If for some , then let ; otherwise, compute in (50) . If and for some and , then let and with . If and for some and , then let and with .

Rule B provides a possibility to cut away all or a large part of the rectangle which is currently investigated by the algorithm procedure.

4. Algorithm and Its Convergence

In this section, a branch-reduce-bound (BRB) algorithm is developed to solve the problem () based on the former discussion. This method needs to solve a sequence of (RLP) problems over partitioned subsets of .

The BRB algorithm is based on partitioning the rectangle into subrectangles, each concerned with a node of the branch and bound tree. Hence, at any stage of the algorithm, suppose that we have a collection of active nodes denoted by , that is, each associated with a rectangle , . For each such node , we will compute a lower bound of the optimal objective function value of () via the optimal value of the RLP() and , so the lower bound of the optimal value of () at stage is given by . We now select an active node to subdivide its associated rectangle into two subrectangles according to the standard branch rule for each new node, reducing it, and then compute the lower bound as before. At the same time, if necessary, we will update the upper bound . Upon fathoming any nonimproving node, we obtain a collection of active nodes for the next stage, and this process is repeated until convergence is obtained.

Algorithm 1. Consider the following steps.

Step  0 (Initialization). Choose the convergence tolerance . Let and . If some feasible solutions are available, add them to and let ; otherwise, let and . Set .

Step  1 (Reduction). (i) Delete every box such that or for some , and denote the remaining still as . If , apply the reduction rule A described in Theorem 5 in Section 3.2 to each box . Let with .

(ii) If , for each box that is currently investigated, we use the reduction rule B in Sub section 3.2 to cut away and denote the left still as .

Step  2 (Bounding). If , begin to do for each the following.

(i) Solve the problem to obtain the optimal solution and the optimal value . Let .

(ii) If for every , then set .

(iii) If , compute a point such that ; otherwise, let .

(iv) If , define the new upper bound , and the best known feasible point is denoted by . Set .

Step  3 (Convergence Checking). Set .

If , then stop: if , the problem is infeasible; otherwise, is the optimal value and is the optimal solution. Otherwise, select an active node for further consideration.

Step  4 (Branching). Divide into two new subrectangles using the standard branch rule and let be the collection of these two subrectangles. Set and return to Step 1.

Convergence Analysis. In this subsection, we give the convergence of the proposed algorithm. Assume that the number of globally optimal solutions of (SGP) is finite. Then the above proposed algorithm either terminates finitely at a globally optimal solution or generates an infinite sequence of iteration nodes. If the algorithm terminates at some iteration , then obviously the point is a globally optimal solution and is the optimal value of problem (). If the algorithm is infinite, its convergence is discussed as follows.

Theorem 9. Assume that the above algorithm is infinite; then it generates an infinite sequence of iterations such that along any infinite branch-and-bound tree any accumulation point of the sequence will be the global minimum of problem ().

Proof. Since the algorithm is infinite, it generates an infinite sequence such that a subsequence of satisfies for . In this case, for every iteration , from [28, 29] there is at least an infinite subsequence of such that

where denotes the feasible region of problem (). We see from [28–30] that is a nondecreasing sequence bounded above by , which guarantees the existence of the limit and . Since is an infinite sequence on a compact set, it follows that there exists a convergent subsequence of satisfying and , where is a subsequence of . The linear functions used in the problem are strongly consistent on . Thus, . All that remains is to show that . Since is a closed set, it follows that . Suppose that . Then there exists some , , such that . Since is continuous, the sequence converges to as . By definition of convergence, such that as , and so when , implies that the problem is infeasible. This contradicts the assumption of . Therefore, ; that is, , and the proof is complete.

5. Numerical Results

To verify the performance of the proposed algorithm, we will give some computational results through ten test problems. The algorithm is coded in Compaq Visual Fortran. The simplex method is applied to solve the relaxation linear programming problems. All test problems are implemented in an Athlon(tm) CPU 2.31 GHz with 960 MB RAM microcomputer.

Example 1 (see [22, 31, 32]). Consider

Example 2 (see [22, 32]). Consider

Example 3 (see [21, 22, 31, 33]). Consider

Example 4 (see [21, 22, 33]). Consider

Example 5 (see [21, 22, 33]). Consider

Example 6 (see [21, 24, 34]). Consider

Example 7 (see [24]). Consider

Example 8 (see [22]). Consider

Example 9 (see [22]). Consider

Example 10 (see [22]). Consider

Tables 1 and 2 summarize the computational results on the above examples, where Iter denotes the number of algorithm iteration.

Table 1: The numerical results for Examples 1–4.

Example
Optimal solution
Optimal value
Iter

1
Ours
(0.1, 9.9999, 8.0, 0.2)
0.7651
58

[22]
(0.1, 9.9999, 8.0, 0.2)
0.7651
132

[31]
(0.1015, 7.331972, 8.0169, 0.2395)
0.9514
175

[32]
(0.1358, 9.9324, 8.6973, 0.2365)
1.0000
171

2
Ours
(7.8922, 0.1002, 450.0000)
7.8922
14

[22]
(12.0475, 0.8167, 444.9416)
12.0475
6472

[32]
(11.9538, 0.8150, 445.1249)
11.9538
67

3
Ours
(149.99608, 29.9530, 3.9456)
−147.6591
156

[22]
(150, 30, 4.9620)
−147.6667
328

[21]
(88.724706796, 7.672652781, 1.317862596)
−83.2497
1829

[31]
(88.6274, 7.9621, 1.3215)
−83.6898
1754

[33]

−83.2497
1809

[24]
(88.875643887, 7.5637589, 1.3124563877)
−83.661573642
754

4
Ours
(43.1601, 44.9944, 69.9968, 1.2241)
460224.676188
23

[22]
(43.0473, 44.9317, 69.9359, 1.1338)
461200.00
968

[21]
()
623249.876
2100

[33]

623249.8752
1717

[24]
(43.0899785, 44.9997852, 66.419945664, 1.1069987564)
468479.996875421
1987

Table 2: The numerical results for Examples 5–10.

Example
Optimal solution
Optimal value
Iter

5
Ours
(78.0, 33.0, 29.9957, 44.9999, 36.7753)
10122.4932
92

[22]
(78.2135, 33.2135, 29.6588, 44.757, 37.6808)
10088.51
122

[21]
(78, 32.9999, 36.7753)
10122.4931
341

[33]

10122.3811
204

[24]
(78, 32.99998, 29.99737, 45, 36.77533)
10122.85643
324

6
Ours
(175.3433, 74.119872, 219.9999)
5651.37804758
2319

[24]
(109.32546781, 84.04821454, 214.32459429)
6217.46548921
612

[21]
(108.734706796, 85.126214158, 204.32459429)
6299.842427922
550

[34]
(107.4, 84.9, 204.5)
6300.00

7
Ours
(0.9999, 0.18181812, 0.98333213)
−10.363636
837

[24]
(0.99712235, 0.18184214, 0.98034321)
−10.305022
3

8
Ours
(8.037732, 8.9999, 9, 9, 9, 1, 1, 1.15686, 1.15686, 1.15686, 50, 0, 1, 50, 50, 0)
156.2196
67

[22]
(8.03772, 9, 9, 9, 9, 1, 1, 1.1568, 1.1568, 1.1568, 50, 0, 1, 50, 50, 0)
156.2196
3

9
Ours
(5.0, 0.021584, 0.044603, 5.0)
5.8894162
3

[22]
(4.99671, 0.02158, 0.044603, 4.99584)
5.888618
12097

10
Ours
(5.0, 5.0, 0.13996, 1.177798, 0.94773)
28660.8648
2831

[22]
(4.992904, 4.99136, 0.1460728, 1.173758, 0.95455)
28745.107539
12014

From the computational results, we can see that the proposed BRB algorithm can solve the problem (SGP) effectively. This illustrates the potential advantage of the proposed algorithm: not only is a feasible optimal solution obtained, but also less computational effort may be required for finding a better objective function value.

6. Conclusion

To globally solve the problem (SGP), a new branch-reduce-bound algorithm is proposed, based on an equivalent monotonic optimization problem and a linear relaxation method. The algorithm can attain the global minimum through the successive refinement of a linear relaxation and the subsequent solutions of a series of linear programming problems. To improve the convergence speed, two range reduction operations are proposed, which can cut away a large part of the region in which the global optimal solution of (SGP) does not exist. The convergence of the algorithm is proved and numerical results are reported to vindicate the feasibility and effectiveness of the proposed algorithm.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This research is supported by the National Natural Science Foundation of China (11171094; 11171368).

References

P. Hansen and B. Jaumard, “Reduction of indefinite quadratic programs to bilinear programs,” Journal of Global Optimization, vol. 2, no. 1, pp. 41–60, 1992.

C. S. Beightler and D. T. Phillips, Applied Geometric Programming, John Wiley & Sons, New York, NY, USA, 1976.

M. Avriel and A. C. Williams, “An extension of geometric programming with applications in engineering optimization,” Journal of Engineering Mathematics, vol. 5, no. 2, pp. 187–194, 1971.

N. K. Jha, “Geometric programming based robot control design,” Computers and Industrial Engineering, vol. 29, no. 1–4, pp. 631–635, 1995.

T. R. Jefferson and C. H. Scott, “Generalized geometric programming applied to problems of optimal control. I. Theory,” Journal of Optimization Theory and Applications, vol. 26, no. 1, pp. 117–129, 1978.

J. G. Ecker, “Geometric programming: methods, computations and applications,” SIAM Review, vol. 22, no. 3, pp. 338–362, 1980.

R. Horst and H. Tuy, Global Optimization: Deterministic Approaches, Springer, Berlin, Germany, 2nd edition, 1993.

C. A. Floudas and V. Visweswaran, “Quadratic optimization,” in Handbook of Global Optimization, Nonconvex Optimization and Its Applications, R. Horst and P. M. Pardalos, Eds., vol. 2, pp. 217–269, Kluwer Academic Publishers, 1995.

Y. K. Sui, “The expansion of functions under transformation and its application to optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 113, no. 3-4, pp. 253–262, 1994.

K. Das, T. K. Roy, and M. Maiti, “Multi-item inventory model with quantity-dependent inventory costs and demand-dependent unit cost under imprecise objective and restrictions: a geometric programming approach,” Production Planning and Control, vol. 11, no. 8, pp. 781–788, 2000.

R. J. Duffin and E. L. Peterson, “Duality theory for geometric programming,” SIAM Journal on Applied Mathematics, vol. 14, pp. 1307–1349, 1966.

R. J. Duffin, E. L. Peterson, and C. Zener, “The geometric inequality and the main lemma,” in Geometric Programming Theory and Applocations, pp. 115–140, John Wiley & Sons, New York, NY, USA, 1967.

R. J. Duffin and E. L. Peterson, “Geometric programming with signomials,” Journal of Optimization Theory and Applications, vol. 11, pp. 3–35, 1973.

U. Passy, “Generalized weighted mean programming,” SIAM Journal on Applied Mathematics, vol. 20, pp. 763–778, 1971.

U. Passy and D. J. Wilde, “Generalized polynomial optimization,” SIAM Journal on Applied Mathematics, vol. 15, pp. 1344–1356, 1967.

K. O. Kortanek, X. Xu, and Y. Ye, “An infeasible interior-point algorithm for solving primal and dual geometric programs,” Mathematical Programming, vol. 76, no. 1, pp. 155–181, 1997.

H. D. Sherali and C. H. Tuncbilek, “A global optimization algorithm for polynomial programming problems using a reformulation-linearization technique,” Journal of Global Optimization, vol. 2, no. 1, pp. 101–112, 1992.

H. D. Sherali and C. H. Tuncbilek, “A reformulation-convexification approach for solving nonconvex quadratic programming problems,” Journal of Global Optimization, vol. 7, no. 1, pp. 1–31, 1995.

H. D. Sherali, “Global optimization of nonconvex polynomial programming problems having rational exponents,” Journal of Global Optimization, vol. 12, no. 3, pp. 267–283, 1998.

C. D. Maranas and C. A. Floudas, “Global optimization in generalized geometric programming,” Computers and Chemical Engineering, vol. 21, no. 4, pp. 351–369, 1997.

P. P. Shen and K. C. Zhang, “Global optimization of signomial geometric programming using linear relaxation,” Applied Mathematics and Computation, vol. 150, no. 1, pp. 99–114, 2004.

P. P. Shen, Y. Ma, and Y. Q. Chen, “A robust algorithm for generalized geometric programming,” Journal of Global Optimization, vol. 41, no. 4, pp. 593–612, 2008.

Y. J. Wang, T. Li, and Z. A. Liang, “A general algorithm for solving generalized geometric programming with nonpositive degree of difficulty,” Computational Optimization and Applications, vol. 44, no. 1, pp. 139–158, 2009.

S. J. Qu, K. C. Zhang, and F. S. Wang, “A global optimization using linear relaxation for generalized geometric programming,” European Journal of Operational Research, vol. 190, no. 2, pp. 345–356, 2008.

H. Tuy, “Effect of the subdivision strategy on convergence and efficiency of some global optimization algorithms,” Journal of Global Optimization, vol. 1, no. 1, pp. 23–36, 1991.

J.-M. Peng and Y.-X. Yuan, “Optimality conditions for the minimization of a quadratic with two quadratic constraints,” SIAM Journal on Optimization, vol. 7, no. 3, pp. 579–594, 1997.

P. P. Shen, X. D. Bai, and W. M. Li, “A new accelerating method for globally solving a class of nonconvex programming problems,” Nonlinear Analysis. Theory, Methods & Applications, vol. 71, no. 7-8, pp. 2866–2876, 2009.

R. Horst, P. M. Pardalos, and N. V. Thoai, Introduction to Global Optimization, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.

R. Horst and H. Tuy, Global Optimization: Deterministic Approaches, Springer, Berlin, Germany, 3rd edition, 2003.

R. Horst, “Deterministic global optimization with partition sets whose feasibility is not known: application to concave minimization, reverse convex constraints, DC-programming, and Lipschitzian optimization,” Journal of Optimization Theory and Applications, vol. 58, no. 1, pp. 11–37, 1988.

S.-J. Qu, K.-C. Zhang, and Y. Ji, “A new global optimization algorithm for signomial geometric programming via Lagrangian relaxation,” Applied Mathematics and Computation, vol. 184, no. 2, pp. 886–894, 2007.

Y. J. Wang and Z. A. Liang, “A deterministic global optimization algorithm for generalized geometric programming,” Applied Mathematics and Computation, vol. 168, no. 1, pp. 722–737, 2005.

P. P. Shen and H. W. Jiao, “A new rectangle branch-and-pruning approach for generalized geometric programming,” Applied Mathematics and Computation, vol. 183, no. 2, pp. 1027–1038, 2006.

M. J. Rijckaert and X. M. Martens, “Comparison of generalized geometric programming algorithms,” Journal of Optimization Theory and Application, vol. 26, pp. 205–241, 1978.