Univariate Global Optimization Research Articles

Piecewise linear bounding functions in univariate global optimization

Piecewise linear bounding functions in univariate global optimization

On the search of the shape parameter in radial basis functions using univariate global optimization methods

In this paper we consider the problem of finding an optimal value of the shape parameter in radial basis function interpolation. In particular, we propose the use of a leave-one-out cross validation (LOOCV) technique combined with univariate global optimization methods, which involve strategies of global optimization with pessimistic improvement (GOPI) and global optimization with optimistic improvement (GOOI). This choice is carried out to overcome serious issues of commonly used optimization routines that sometimes result in shape parameter values are not globally optimal. New locally-biased versions of geometric and information Lipschitz global optimization algorithms are presented. Numerical experiments and applications to real-world problems show a promising performance and efficacy of the new algorithms, called LOOCV-GOPI and LOOCV-GOOI, in comparison with their direct competitors.

Steklov regularization and trajectory methods for univariate global optimization

We introduce a new regularization technique, using what we refer to as the Steklov regularization function, and apply this technique to devise an algorithm that computes a global minimizer of univariate coercive functions. First, we show that the Steklov regularization convexifies a given univariate coercive function. Then, by using the regularization parameter as the independent variable, a trajectory is constructed on the surface generated by the Steklov function. For monic quartic polynomials, we prove that this trajectory does generate a global minimizer. In the process, we derive some properties of quartic polynomials. Comparisons are made with a previous approach which uses a quadratic regularization function. We carry out numerical experiments to illustrate the working of the new method on polynomials of various degree as well as a non-polynomial function.

Combination of two underestimators for univariate global optimization

In this work, we propose a new underestimator in branch and bound algorithm for solving univariate global optimization problems. The new underestimator is a combination of two underestimators, the classical one used in αBB method (see Androulakis et al. [J. Glob. Optim. 7 (1995) 337–3637]) and the quadratic underestimator developed in Hoai An and Ouanes [RAIRO: OR 40 (2006) 285–302]. We show that the new underestimator is tighter than the two underestimators. A convex/concave test is used to accelerate the convergence of the proposed algorithm. The convergence of our algorithm is shown and a set of test problems given in Casado et al. [J. Glob. Optim. 25 (2003) 345–362] are solved efficiently.

New technique for solving univariate global optimization

In this paper, a new global optimization method is proposed for an optimization problem with twice differentiable objective function a single variable with box constraint. The method employs a difference of linear interpolant of the objective and a concave function, where the former is a continuous piecewise convex quadratic function underestimator. The main objectives of this research are to determine the value of the lower bound that does not need an iterative local optimizer. The proposed method is proven to have a finite convergence to locate the global optimum point. The numerical experiments indicate that the proposed method competes with another covering methods.

Derivative-Free Local Tuning and Local Improvement Techniques Embedded in the Univariate Global Optimization

Geometric and information frameworks for constructing global optimization algorithms are considered, and several new ideas to speed up the search are proposed. The accelerated global optimization methods automatically realize a local behavior in the promising subregions without the necessity to stop the global optimization procedure. Moreover, all the trials executed during the local phases are used also in the course of the global ones. The resulting geometric and information global optimization methods have a similar structure, and a smart mixture of new and traditional computational steps leads to 22 different global optimization algorithms. All of them are studied and numerically compared on three test sets including 120 benchmark functions and 4 applied problems.

NEW UNDERESTIMATOR FOR MULTIVARIATE GLOBAL OPTIMIZATION WITH BOX CONSTRAINTS

The paper is concerned with the multivariate global optimization with box constraints. A new underestimator is investigated for twice contin- uously differentiable function on a box which is an extension of the approach developed in (5) for univariate global optimization.

Acceleration of Univariate Global Optimization Algorithms Working with Lipschitz Functions and Lipschitz First Derivatives

This paper deals with two kinds of the one-dimensional global optimization problems over a closed finite interval: (i) the objective function $f(x)$ satisfies the Lipschitz condition with a constant $L$; (ii) the first derivative of $f(x)$ satisfies the Lipschitz condition with a constant $M$. In the paper, six algorithms are presented for the case (i) and six algorithms for the case (ii). In both cases, auxiliary functions are constructed and adaptively improved during the search. In the case (i), piece-wise linear functions are constructed and in the case (ii) smooth piece-wise quadratic functions are used. The constants $L$ and $M$ either are taken as values known a priori or are dynamically estimated during the search. A recent technique that adaptively estimates the local Lipschitz constants over different zones of the search region is used to accelerate the search. A new technique called the \emph{local improvement} is introduced in order to accelerate the search in both cases (i) and (ii). The algorithms are described in a unique framework, their properties are studied from a general viewpoint, and convergence conditions of the proposed algorithms are given. Numerical experiments executed on 120 test problems taken from the literature show quite a promising performance of the new accelerating techniques.

An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate for Twice Continuously Differentiable Functions

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a twice continuously differentiable function.

Univariate geometric Lipschitz global optimization algorithms

In this survey, univariate global optimization problems are considered where the objective function or its first derivative can be multiextremal black-box costly functions satisfying the Lipschitz condition over an interval. Such problems are frequently encountered in practice. A number of geometric methods based on constructing auxiliary functions with the usage of different estimates of the Lipschitz constants are described in the paper.

An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate under the Wiener Measure

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a random objective function distributed according to the Wiener measure.

Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint

The purpose of this paper is to demonstrate that, for globally minimize one dimensional nonconvex problems with both twice differentiable function and constraint, we can propose an efficient algorithm based on Branch and Bound techniques. The method is first displayed in the simple case with an interval constraint. The extension is displayed afterwards to the general case with an additional nonconvex twice differentiable constraint. A quadratic bounding function which is better than the well known linear underestimator is proposed while w-subdivision is added to support the branching procedure. Computational results on several and various types of functions show the efficiency of our algorithms and their superiority with respect to the existing methods.

Univariate Global Optimization with Multiextremal Non-Differentiable Constraints Without Penalty Functions

This paper proposes a new algorithm for solving constrained global optimization problems where both the objective function and constraints are one-dimensional non-differentiable multiextremal Lipschitz functions. Multiextremal constraints can lead to complex feasible regions being collections of isolated points and intervals having positive lengths. The case is considered where the order the constraints are evaluated is fixed by the nature of the problem and a constraint i is defined only over the set where the constraint i−1 is satisfied. The objective function is defined only over the set where all the constraints are satisfied. In contrast to traditional approaches, the new algorithm does not use any additional parameter or variable. All the constraints are not evaluated during every iteration of the algorithm providing a significant acceleration of the search. The new algorithm either finds lower and upper bounds for the global optimum or establishes that the problem is infeasible. Convergence properties and numerical experiments showing a nice performance of the new method in comparison with the penalty approach are given.

Optimal centers in branch-and-prune algorithms for univariate global optimization

We present an interval branch-and-prune algorithm for computing verified enclosures for the global minimum and all global minimizers of univariate functions subject to bound constraints. The algorithm works within the branch-and-bound framework and uses first order information of the objective function. In this context, we investigate valuable properties of the optimal center of a mean value form and prove optimality. We also establish an inclusion function selection criterion between natural interval extension and an optimal mean value form for the bounding process. Based on optimal centers, we introduce linear (inner and outer) pruning steps that are responsible for the branching process. The proposed algorithm incorporates the above techniques in order to accelerate the search process. Our algorithm has been implemented and tested on a test set and compared with three other methods. The method suggested shows a significant improvement on previous methods for the numerical examples solved.

Index branch-and-bound algorithm for Lipschitz univariate global optimization with multiextremal constraints

In this paper, Lipschitz univariate constrained global optimization problems where both the objective function and constraints can be multiextremal are considered. The constrained problem is reduced to a discontinuous unconstrained problem by the index scheme without introducing additional parameters or variables. A Branch-and-Bound method that does not use derivatives for solving the reduced problem is proposed. The method either determines the infeasibility of the original problem or finds lower and upper bounds for the global solution. Not all the constraints are evaluated during every iteration of the algorithm, providing a significant acceleration of the search. Convergence conditions of the new method are established. Extensive numerical experiments are presented.

Acceleration Tools for Diagonal Information Global Optimization Algorithms

In this paper we face a classical global optimization problem—minimization of a multiextremal multidimensional Lipschitz function over a hyperinterval. We introduce two new diagonal global optimization algorithms unifying the power of the following three approaches: efficient univariate information global optimization methods, diagonal approach for generalizing univariate algorithms to the multidimensional case, and local tuning on the behaviour of the objective function (estimates of the local Lipschitz constants over different subregions) during the global search. Global convergence conditions of a new type are established for the diagonal information methods. The new algorithms demonstrate quite satisfactory performance in comparison with the diagonal methods using only global information about the Lipschitz constant.

An improved univariate global optimization algorithm with improved linear lower bounding functions

Recently linear lower bounding functions (LLBF's) were proposed and used to find e-global minima. Basically an LLBF over an interval is a linear function which lies below a given function over the interval and matches the function value at one end point. By comparing it with the best function value found, it can be used to eliminate subregions which do not contain e-global minima. To develop a more efficient LLBF algorithm, two important issues need to be addressed: how to construct a better LLBF and how to use it efficiently. In this paper, an improved LLBF for factorable functions overn-dimensional boxes is derived, in the sense that the new LLBF is always better than those in [3] for continuously differentiable functions. Exploration of the properties of the LLBF enables us to develop a new LLBF-based univariate global optimization algorithm, which is again better than those in [3]. Numerical results on some standard test functions indicate the high potential of our algorithm.

Probabilistic production costing simulation of a thermal system with a pumped-storage power plant

The paper presents a model and two algorithms for the calculation of the additional pumping generation of thermal power plants and the discharging energy of a pumped-storage power plant in a thermal system. Both algorithms lead to the solution of a univariate global optimization problem. One of these problems provides an optimal result. The other is suboptimal, as it is based on a lower estimation for the absolute value of the derivative of the equivalent load duration curve. The proposed algorithms can be applied within any load duration curve based probabilistic method, and the practical solution of any of these problems depends on the type of convolution method used. The pitfalls of using load duration curves is well known from the literature, therefore the proposed methods can be used only for medium- and long-term planning purposes, and the results do not have operative meaning for the subperiods of the planning horizon.

Cord-slope form of Taylor's expansion in univariate global optimization

Interval arithmetic and Taylor's formula can be used to bound the slope of the cord of a univariate function at a given point. This leads in turn to bounding the values of the function itself. Computing such bounds for the function, its first and second derviatives, allows the determination of intervals in which this function cannot have a global minimum. Exploiting this information together with a simple branching rule yields an efficient algorithm for global minimization of univariate functions. Computational experience is reported.

Fitting three-parameter lognormal models by numerical global optimization — an improved algorithm

The theoretical maximum likelihood point estimates of the parameters of the three-parameter lognormal distribution (LD) for complete, ungrouped samples are infeasible values for which the likelihood function is positively infinite. This has prompted the search for usable point estimates which are called local-maximum likelihood estimates (LMLE's) and which correspond to the largest finite local maximum of the log-likelihood function (LLF). Previously published work has shown that an effective way to obtain LMLE's is to numerically maximize the conditional LLF of the observations using sequential interior penalty and barrier function methods. These methods are robust but necessitate the numerical solution of a sequence of maximization problems. This paper describes an algorithm for computing LMLE's which involves only a single numerical maximization of a suitably transformed conditional LLF. The results of computational tests indicate that the algorithm, when implemented using computationally efficient and reliable univariate global optimization methods, is a computationally robust method for computing LMLE's of LD parameters.