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.     

Two families of liapunov functions for functional differential systems

Two families of Liapunov functions are employed to study the global stability and boundedness of functional differential systems. New stability and boundedness theorems are obtained. Applications of these theorems to some nonlinear differential systems with infinite delay are discussed. Project supported by the National Science Foundation of China Under Grants 69871005     

Geometric criteria for the nonexistence of cycles in Gause-type predator-prey systems

The global stability of a multi-species interacting system has apparently important biological implications. In this paper we study the global stability of Gause-type predator-prey models by providing new criteria for the nonexistence of cycles and limit cycles. Our criteria have clear geometrical interpretations and are easier to apply than other methods employed in recent studies. Using these criteria and related techniques we are able to develop new results on the existence and uniqueness of cycles in Gause-type models with various growth and response functions.

     

Global results for an HIV/AIDS model with multiple susceptible classes and nonlinear incidence

In this paper, an HIV/AIDS epidemic model is proposed in which there are two susceptible classes. Two types of general nonlinear incidence functions are employed to depict the scenarios of infection among cautious and incautious individuals. Qualitative analyses are performed, in terms of the basic reproduction number $\R_0$, to gain the global dynamics of the model: the disease-free equilibrium is of global asymptotic stability when $\R_0\leq 1$; a unique endemic equilibrium exists and globally asymptotically stable $\R_0> 1$. The introduction of cautious susceptible and the resulting multiple transmission functions has positive effect on HIV/AIDS prevalence. Numerical simulations are carried out to illustrate and extend the obtained analytical results.     

Fixed point approach for weakly asymptotic stability of fractional differential inclusions involving impulsive effects

We prove the global solvability and weakly asymptotic stability for a semilinear fractional differential inclusion subject to impulsive effects by analyzing behavior of its solutions on the half-line. Our analysis is based on a fixed point principle for condensing multi-valued maps, which is employed for solution operator acting on the space of piecewise continuous functions. The obtained results will be applied to a lattice fractional differential system.     

Fast construction of constant bound functions for sparse polynomials

A new method for the representation and computation of Bernstein coefficients of multivariate polynomials is presented. It is known that the coefficients of the Bernstein expansion of a given polynomial over a specified box of interest tightly bound the range of the polynomial over the box. The traditional approach requires that all Bernstein coefficients are computed, and their number is often very large for polynomials with moderately-many variables. The new technique detailed represents the coefficients implicitly and uses lazy evaluation so as to render the approach practical for many types of non-trivial sparse polynomials typically encountered in global optimization problems; the computational complexity becomes nearly linear with respect to the number of terms in the polynomial, instead of exponential with respect to the number of variables. These range-enclosing coefficients can be employed in a branch-and-bound framework for solving constrained global optimization problems involving polynomial functions, either as constant bounds used for box selection, or to construct affine underestimating bound functions. If such functions are used to construct relaxations for a global optimization problem, then sub-problems over boxes can be reduced to linear programming problems, which are easier to solve. Some numerical examples are presented and the software used is briefly introduced.     

Synchronization of chaos in non-identical parametrically excited systems

In this paper, we investigate the synchronization of chaotic systems consisting of non-identical parametrically excited oscillators. The active control technique is employed to design control functions based on Lyapunov stability theory and Routh–Hurwitz criteria so as to achieve global chaos synchronization between a parametrically excited gyroscope and each of the parametrically excited pendulum and Duffing oscillator. Numerical simulations are implemented to verify the results.     

Evolutionary Operators in Global Optimization with Dynamic Search Trajectories

One of the most commonly encountered approaches for the solution of unconstrained global optimization problems is the application of multi-start algorithms. These algorithms usually combine already computed minimizers and previously selected initial points, to generate new starting points, at which, local search methods are applied to detect new minimizers. Multi-start algorithms are usually terminated once a stochastic criterion is satisfied. In this paper, the operators of the Differential Evolution algorithm are employed to generate the starting points of a global optimization method with dynamic search trajectories. Results for various well-known and widely used test functions are reported, supporting the claim that the proposed approach improves drastically the performance of the algorithm, in terms of the total number of function evaluations required to reach a global minimizer.     

Machine learning for global optimization

In this paper we introduce the LeGO (Learning for Global Optimization) approach for global optimization in which machine learning is used to predict the outcome of a computationally expensive global optimization run, based upon a suitable training performed by standard runs of the same global optimization method. We propose to use a Support Vector Machine (although different machine learning tools might be employed) to learn the relationship between the starting point of an algorithm and the final outcome (which is usually related to the function value at the point returned by the procedure). Numerical experiments performed both on classical test functions and on difficult space trajectory planning problems show that the proposed approach can be very effective in identifying good starting points for global optimization.     

A kriging based method for the solution of mixed-integer nonlinear programs containing black-box functions

In this paper a new methodology is developed for the solution of mixed-integer nonlinear programs under uncertainty whose problem formulation is complicated by both noisy variables and black-box functions representing a lack of model equations. A branch-and-bound framework is employed to handle the integer complexity whereby the solution to the relaxed nonlinear program subproblem at each node is obtained using both global and local information. Global information is obtained using kriging models which are used to identify promising neighborhoods for local search. Response surface methodology (RSM) is then employed whereby local models are sequentially optimized to refine the problem’s lower and upper bounds. This work extends the capabilities of a previously developed kriging-response surface method enabling a wider class of problems to be addressed containing integer decisions and black box models. The proposed algorithm is applied to several small process synthesis examples and its effectiveness is evaluated in terms of the number of function calls required, number of times the global optimum is attained, and computational time.     

A New Trust-Region Algorithm for Equality Constrained Optimization

We present a new trust-region algorithm for solving nonlinear equality constrained optimization problems. Quadratic penalty functions are employed to obtain global convergence. At each iteration a local change of variables is performed to improve the ability of the algorithm to follow the constraint level set. Under certain assumptions we prove that this algorithm globally converges to a point satisfying the second-order necessary optimality conditions. Results of preliminary numerical experiments are reported.     

Finding all solutions of nonlinearly constrained systems of equations

A new approach is proposed for finding all-feasible solutions for certain classes of nonlinearly constrained systems of equations. By introducing slack variables, the initial problem is transformed into a global optimization problem (P) whose multiple global minimum solutions with a zero objective value (if any) correspond to all solutions of the initial constrained system of equalities. All-globally optimal points of (P) are then localized within a set of arbitrarily small disjoint rectangles. This is based on a branch and bound type global optimization algorithm which attains finite-convergence to each of the multiple global minima of (P) through the successive refinement of a convex relaxation of the feasible region and the subsequent solution of a series of nonlinear convex optimization problems. Based on the form of the participating functions, a number of techniques for constructing this convex relaxation are proposed. By taking advantage of the properties of products of univariate functions, customized convex lower bounding functions are introduced for a large number of expressions that are or can be transformed into products of univariate functions. Alternative convex relaxation procedures involve either the difference of two convex functions employed in BB [23] or the exponential variable transformation based underestimators employed for generalized geometric programming problems [24]. The proposed approach is illustrated with several test problems. For some of these problems additional solutions are identified that existing methods failed to locate.     

A class of filled functions for finding global minimizers of a function of several variables

This paper is concerned with filled function methods for finding global minimizers of a function of several variables. A class of filled functions is defined. The advantages and disadvantages of every filled function in the class are analyzed. The best one in this class is pointed out. The idea behind constructing a better filled function is given and employed to construct the class of filled functions. A method is also explored on how to locate minimizers or saddle points of a filled function through only the use of the gradient of a function.The authors are indebted to Dr. L. C. W. Dixon for stimulating discussions.     

A local linear embedding module for evolutionary computation optimization

A Local Linear Embedding (LLE) module enhances the performance of two Evolutionary Computation (EC) algorithms employed as search tools in global optimization problems. The LLE employs the stochastic sampling of the data space inherent in Evolutionary Computation in order to reconstruct an approximate mapping from the data space back into the parameter space. This allows to map the target data vector directly into the parameter space in order to obtain a rough estimate of the global optimum, which is then added to the EC generation. This process is iterated and considerably improves the EC convergence. Thirteen standard test functions and two real-world optimization problems serve to benchmark the performance of the method. In most of our tests, optimization aided by the LLE mapping outperforms standard implementations of a genetic algorithm and a particle swarm optimization. The number and ranges of functions we tested suggest that the proposed algorithm can be considered as a valid alternative to traditional EC tools in more general applications. The performance improvement in the early stage of the convergence also suggests that this hybrid implementation could be successful as an initial global search to select candidates for subsequent local optimization.     

Dynamics of a class of Cohen–Grossberg neural networks with time-varying delays

Without assuming the boundedness, monotonicity, and differentiability of activation functions and any symmetry of interconnections, we establish some sufficient conditions for the global exponential stability of a unique equilibrium and the existence of periodic solution for the Cohen–Grossberg neural network with time-varying delays. Brouwer's fixed point theorem, matrix theory, a continuation theorem of the coincidence degree and inequality analysis are employed. Our results are all independent of the delays and maybe more convenient to design a circuit network.     

Curve Fitting with Piecewise Polynomials

Polynomials and splines are frequently used as approximatingfunctions in curve fitting. This paper discusses curve fittingwith the more general piecewise polynomials employed as theapproximating functions. Various representations of piecewisepolynomials are discussed and one particular form is selectedas providing a basis for a well-conditioned formulation of theleast-squares curve-fitting problem. Continuous and discontinuousapproximations with fixed and free knots are considered fromthe points of view of existence, uniqueness and characterizationof solutions. Two general algorithms are suggested for the continuouscase with arbitrary degree and continuity with fixed knots.The imposition of end conditions is shown to be straightforward.Using the principles of dynamic programming a method is proposedthat enables global solutions to be obtained economically inthe case of discontinuous approximating functions with freeknots.     

Generalized Flows and Singular ODEs on Differentiable Manifolds

Based on the concept of manifold-valued generalized functions, we initiate a study of nonlinear ordinary differential equations with singular (in particular: distributional) right-hand sides in a global setting. After establishing several existence and uniqueness results for solutions of such equations and flows of singular vector fields, we compare the solution concept employed here with the purely distributional setting. Finally, we derive criteria securing that a sequence of smooth flows corresponding to the regularization of a given singular vector field converges to a measurable limiting flow.     

Analytical variance based global sensitivity analysis for models with correlated variables

In order to quantitatively analyze the variance contributions by correlated input variables to the model output, variance based global sensitivity analysis (GSA) is analytically derived for models with correlated variables. The derivation is based on the input-output relationship of tensor product basis functions and the orthogonal decorrelation of the correlated variables. Since the tensor product basis function based simulator is widely used to approximate the input-output relationship of complicated structure, the analytical solution of the variance based global sensitivity is especially applicable to engineering practice problems. The polynomial regression model is employed as an example to derive the analytical GSA in detail. The accuracy and efficiency of the analytical solution of GSA are validated by three numerical examples, and engineering application of the derived solution is demonstrated by carrying out the GSA of the riveting and two dimension fracture problem.     

An algorithm for a class of nonlinear complementarity problems with non-Lipschitzian functions

In this paper, we focus on solving a class of nonlinear complementarity problems with non-Lipschitzian functions. We first introduce a generalized class of smoothing functions for the plus function. By combining it with Robinson's normal equation, we reformulate the complementarity problem as a family of parameterized smoothing equations. Then, a smoothing Newton method combined with a new nonmonotone line search scheme is employed to compute a solution of the smoothing equations. The global and local superlinear convergence of the proposed method is proved under mild assumptions. Preliminary numerical results obtained applying the proposed approach to nonlinear complementarity problems arising in free boundary problems are reported. They show that the smoothing function and the nonmonotone line search scheme proposed in this paper are effective.