A distributed global optimization method for packing problems

Packing optimization problems aim to seek the best way of placing a given set of rectangular cartons within a minimum volume rectangular container. Currently, packing optimization methods either have difficulty in finding a globally optimal solution or are computationally inefficient, because models involve too many 0–1 variables and because use of just a single computer. This study proposes a distributed computation method for solving a packing problem by a set of personal computers via the Internet. First, the traditional packing optimization model is converted into an equivalent model containing many fewer 0–1 variables. Then the model is decomposed into several sub-problems by dividing the objective value into many intervals. Each of these sub-problems is a linearized logarithmic program expressed as a linear mixed 0–1 problem. The whole problem is solvable and reaches a globally optimal solution. The numerical examples demonstrate that the proposed method can obtain the global optimum of a packing problem effectively.     

A global optimization approach for solving three-dimensional open dimension rectangular packing problems

The three-dimensional open dimension rectangular packing problem (3D-ODRPP) aims to pack a set of given rectangular boxes into a large rectangular container of minimal volume. This problem is an important issue in the shipping and moving industries. All the boxes can be any rectangular stackable objects with different sizes and may be freely rotated. The 3D-ODRPP is usually formulated as a mixed-integer non-linear programming problem. Most existing packing optimization methods cannot guarantee to find a globally optimal solution or are computationally inefficient. Therefore, this paper proposes an efficient global optimization method that transforms a 3D-ODRPP as a mixed-integer linear program using fewer extra 0–1 variables and constraints compared to existing deterministic approaches. The reformulated model can be solved to obtain a global optimum. Experimental results demonstrate the computational efficiency of the proposed approach in globally solving 3D-ODRPPs drawn from the literature and the practical applications.     

Solving circle packing problems by global optimization: Numerical results and industrial applications

A (general) circle packing is an optimized arrangement of N arbitrary sized circles inside a container (e.g., a rectangle or a circle) such that no two circles overlap. In this paper, we present several circle packing problems, review their industrial applications, and some exact and heuristic strategies for their solution. We also present illustrative numerical results using ‘generic’ global optimization software packages. Our work highlights the relevance of global optimization in solving circle packing problems, and points towards the necessary advancements in both theory and numerical practice.     

A convexification method for a class of global optimization problems with applications to reliability optimization

A convexification method is proposed for solving a class of global optimization problems with certain monotone properties. It is shown that this class of problems can be transformed into equivalent concave minimization problems using the proposed convexification schemes. An outer approximation method can then be used to find the global solution of the transformed problem. Applications to mixed-integer nonlinear programming problems arising in reliability optimization of complex systems are discussed and satisfactory numerical results are presented.     

Matrix monotonicity and self-concordance: how to handle quantum entropy in optimization problems

Let g be a continuously differentiable function whose derivative is matrix monotone on the positive semi-axis. Such a function induces a function \(\varphi (x)=\mathrm{tr}\, \big (g(x)\big )\) on the cone of squares of an arbitrary Euclidean Jordan algebra. We show that \(\varphi (x) - \ln \det (x)\) is a self-concordant function on the interior of the cone. We also show that \( -\ln (t-\varphi (x))-\ln \det (x)\) is \((r+1)\)-self-concordant barrier on the epigraph of \(\varphi ,\) where r is the rank of the Jordan algebra. The case \(\phi (x)=\mathrm{tr(x\ln x)}\) is discussed in detail.     

A hybrid method for solving multi-objective global optimization problems

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of optimums, which constitute the so called Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an approximation to this front. However, most problems of this kind cannot be solved exactly because they have very large and highly complex search spaces. The objective of this work is to compare the performance of a new hybrid method here proposed, with several well-known multi-objective evolutionary algorithms (MOEA). The main attraction of these methods is the integration of selection and diversity maintenance. Since it is very difficult to describe exactly what a good approximation is in terms of a number of criteria, the performance is quantified with adequate metrics that evaluate the proximity to the global Pareto-front. In addition, this work is also one of the few empirical studies that solves three-objective optimization problems using the concept of global Pareto-optimality.     

A global optimization method for nonconvex separable programming problems

Conventional methods of solving nonconvex separable programming (NSP) problems by mixed integer programming methods requires adding numerous 0–1 variables. In this work, we present a new method of deriving the global optimum of a NSP program using less number of 0–1 variables. A separable function is initially expressed by a piecewise linear function with summation of absolute terms. Linearizing these absolute terms allows us to convert a NSP problem into a linearly mixed 0–1 program solvable for reaching a solution which is extremely close to the global optimum.     

全局优化问题的一个新的无参数填充函数

求全局最优化问题的填充函数算法被提出以来,参数的选取和调整一直是制约算法有效性的因素.如何在实际的计算过程中选取合适的参数,直接影响和决定了运算速度和效率.因此,构造不含参数的填充函数就显得极为重要.提出一个新的无参数的填充函数,对其理论性质进行了分析,并给出相应的填充函数算法,数值计算验证了算法的有效性.     

On average complexity of global optimization problems

We discuss the average case complexity of global optimization problems. By the average complexity, we roughly mean the amount of work needed to solve the problem with the expected error not exceeding a preassigned error demand. The expectation is taken with respect to a probability measure on a classF of objective functions.Since the distribution of the maximum, max _x f (x), is known only for a few nontrivial probability measures, the average case complexity of optimization is still unknown. Although only preliminary results are available, they indicate that on the average, optimization is not as hard as in the worst case setting. In particular, there are instances, where global optimization is intractable in the worst case, whereas it is tractable on the average.We stress, that the power of the average case approach is proven by exhibiting upper bounds on the average complexity, since the actual complexity is not known even for relatively simple instances of global optimization problems. Thus, we do not know how much easier global optimization becomes when the average case approach is utilized.Research partially supported by the National Science Foundation under Grant CCR-89-0537.     

An efficient deterministic optimization approach for rectangular packing problems

The rectangular packing problem aims to seek the best way of placing a given set of rectangular pieces within a large rectangle of minimal area. Such a problem is often constructed as a quadratic mixed-integer program. To find the global optimum of a rectangular packing problem, this study transforms the original problem as a mixed-integer linear programming problem by logarithmic transformations and an efficient piecewise linearization approach that uses a number of binary variables and constraints logarithmic in the number of piecewise line segments. The reformulated problem can be solved to obtain an optimal solution within a tolerable error. Numerical examples demonstrate the computational efficiency of the proposed method in globally solving rectangular packing problems.     

A note on global Pareto optimality in multicriteria optimization problems

We establish a range of sufficient conditions for (proper) Pareto optimality of all points in natural domains of multicriteria optimization problems.     

A vector linear programming approach for certain global optimization problems

A large part of the European natural gas imports originates from unstable regions exposed to the risk of supply failure due to economical and political reasons. This has increased the concerns on the security of supply in the European natural gas market. In this paper, we analyze the security of external supply of the Italian gas market that mainly relies on natural gas imports to cover its internal demand. To this aim, we develop an optimization problem that describes the equilibrium state of a gas supply chain where producers, mid-streamers, and final consumers exchange natural gas and liquefied natural gas. Both long-term contracts (LTCs) and spot pricing systems are considered. Mid-streamers are assumed to be exposed to the external supply risk, which is estimated with indicators that we develop starting from those already existing in the literature. In addition, we investigate different degrees of mid-streamers’ flexibility by comparing a situation where mid-streamers fully satisfy the LTC volume clause (“No FLEX” assumption) to a case where the fulfillment of this volume clause is not compulsory (“FLEX” assumption). Our analysis shows that, in the “No FLEX” case, mid-streamers do not significantly change their supplying choices even when the external supply risk is considered. Under this assumption, they face significant profit losses that, instead, disappear in the “FLEX” case when mid-streamers are more flexible and can modify their supply mix. However, the “FLEX” strategy limits the gas availability in the supply chain leading to a curtailment of the social welfare.     

A new filled function algorithm for constrained global optimization problems

A new filled function with one parameter is proposed for solving constrained global optimization problems without the coercive condition, in which the filled function contains neither exponential term nor fractional term and is easy to be calculated. A corresponding filled function algorithm is established based on analysis of the properties of the filled function. At last, we perform numerical experiments on some typical test problems using the algorithm and the detailed numerical results show that the algorithm is effective.     

A new hybrid classical-quantum algorithm for continuous global optimization problems

Grover’s algorithm can be employed in global optimization methods providing, in some cases, a quadratic speedup over classical algorithms. This paper describes a new method for continuous global optimization problems that uses a classical algorithm for finding a local minimum and Grover’s algorithm to escape from this local minimum. Such algorithms will be useful when quantum computers of reasonable size are available. Simulations with testbed functions and comparisons with algorithms from the literature are presented.     

基于随机搜索的一个约束优化问题的全局收敛算法

讨论了具有一般约束的全局优化问题,给出该问题的一个随机搜索算法,证明了该算法依概率1收敛到问题的全局最优解．数值结果显示该方法是有效的．     

On Schwarz-type smoothers for saddle point problems with applications to PDE-constrained optimization problems

In this paper we consider a (one-shot) multigrid strategy for solving the discretized optimality system (KKT system) of a PDE-constrained optimization problem. In particular, we discuss the construction of an additive Schwarz-type smoother for a certain class of optimal control problems. A rigorous multigrid convergence analysis is presented. Numerical experiments are shown which confirm the theoretical results. The work was supported by the Austrian Science Fund (FWF) under grant SFB 013/F1309.     

A saddle point characterization of efficient solutions for interval optimization problems

In this article, we attempt to characterize efficient solutions of constrained interval optimization problems. Towards this aim, at first, we study a scalarization characterization to capture efficient solutions. Then, with the help of saddle point of a newly introduced Lagrangian function, we investigate efficient solutions of an interval optimization problem. Several parts of the results are supported with numerical and pictorial illustration.     

A branch-and-reduce approach to global optimization

This paper presents valid inequalities and range contraction techniques that can be used to reduce the size of the search space of global optimization problems. To demonstrate the algorithmic usefulness of these techniques, we incorporate them within the branch-and-bound framework. This results in a branch-and-reduce global optimization algorithm. A detailed discussion of the algorithm components and theoretical properties are provided. Specialized algorithms for polynomial and multiplicative programs are developed. Extensive computational results are presented for engineering design problems, standard global optimization test problems, univariate polynomial programs, linear multiplicative programs, mixed-integer nonlinear programs and concave quadratic programs. For the problems solved, the computer implementation of the proposed algorithm provides very accurate solutions in modest computational time.