Collection Of Test Problems Research Articles

A New Variant of the Conjugate Descent Method for Solving Unconstrained Optimization Problems and Applications

Unconstrained optimization problems have a long history in computational mathematics and have been identified as being among the crucial problems in the fields of applied sciences, engineering, and management sciences. In this paper, a new variant of the conjugate descent method for solving unconstrained optimization problems is introduced. The proposed algorithm can be seen as a modification of the popular conjugate descent (CD) algorithm of Fletcher. The algorithm of the proposed method is well-defined, and the sequence of the directions of search is shown to be sufficiently descending. The convergence result of the proposed method is discussed under the common standard conditions. The proposed algorithm together with some existing ones in the literature is implemented to solve a collection of benchmark test problems. Numerical experiments conducted show the performance of the proposed method is very encouraging. Furthermore, an additional efficiency evaluation is carried out on problems arising from signal processing and it works well.

A descent subgradient method using Mifflin's line search for nonsmooth nonconvex optimization

We propose a descent subgradient algorithm for minimizing a function f : R n → R , assumed to be locally Lipschitz, but not necessarily smooth or convex. To find an effective descent direction, the Goldstein ε-subdifferential is approximated through an iterative process. The method enjoys a new two-point variant of Mifflin's line search in which the subgradients are arbitrary. Thus, the line search procedure is easy to implement. Moreover, in comparison to bundle methods, the quadratic subproblems have a simple structure, and to handle nonconvexity the proposed method requires no algorithmic modification. We study the global convergence of the method and prove that any accumulation point of the generated sequence is Clarke stationary, assuming that the objective f is weakly upper semismooth. We illustrate the efficiency and effectiveness of the proposed algorithm on a collection of academic and semi-academic test problems.

Approximating sparse Hessian matrices using large-scale linear least squares

Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readily available. Existing methods for approximating large sparse Hessian matrices have limitations. To try and overcome these, we propose a novel approach that reformulates the problem as the solution of a large linear least squares problem. The least squares problem is sparse but can include a number of rows that contain significantly more entries than other rows and are regarded as dense. We exploit recent work on solving such problems using either the normal equations or an augmented system to derive a robust approach for computing approximate sparse Hessian matrices. Example sparse Hessians from the CUTEst test problem collection for optimization illustrate the effectiveness and robustness of the new method.

A new type of improved third order WENO scheme with finite difference framework

In this paper, we present a novel variety of third order finite difference weighted essentially non-oscillatory (WENO) scheme based on WENO-ZQ scheme to approximate the computational solutions of one- and two-dimensional non-linear hyperbolic conservation laws. The new reconstruction process of fluxes is the convex combination of a second degree polynomial and two linear polynomials. We apply a new modified global smoothness indicator into the non-linear weights. We also use the power parameter ‘p’ to balance the accuracy of the proposed scheme. This novel third order WENO scheme applies three points data {xi−1,xi,xi+1} same as WENO-JS3 and WENO-Z3 schemes. In the proposed scheme, the main advantages are that the associated linear weights can be taken as any positive numbers on condition their summation is one. Also, it is easy to be used in multi-dimensional engineering problems. We implement the time discretization by using third order accurate strong stability preserving Runge–Kutta method. The extensive computational experiments of a collection of benchmark test problems are provided to demonstrate high resolution, robustness and absolute truncation errors in L1 and L∞ norms. We observe from the results that the new scheme contains better capability in performance than WENO-PZ3 scheme.

On the application of Jacobian-free Riemann solvers for relativistic radiation magnetohydrodynamics under M1 closure

Radiative transfer plays a major role in high-energy astrophysics. In multiple scenarios and in a broad range of energy scales, the coupling between matter and radiation is essential to understand the interplay between theory, observations and numerical simulations. In this paper, we present a novel scheme for solving the equations of radiation relativistic magnetohydrodynamics within the parallel code Lóstrego. These equations, which are formulated taking successive moments of the Boltzmann radiative transfer equation, are solved under the grey-body approximation and the M1 closure using an IMEX time integration scheme. The main novelty of our scheme is that we introduce for the first time in the context of radiation magnetohydrodynamics a family of Jacobian-free Riemann solvers based on internal approximations to the Polynomial Viscosity Matrix, which were demonstrated to be robust and accurate for non-radiative applications. The robustness and the limitations of the new algorithms are tested by solving a collection of one-dimensional and multi-dimensional test problems, both in the free-streaming and in the diffusion radiation transport limits. Due to its stable performance, the applicability of the scheme presented in this paper to real astrophysical scenarios in high-energy astrophysics is promising. In future simulations, we expect to be able to explore the dynamical relevance of photon-matter interactions in the context of relativistic jets and accretion discs, from microquasars and AGN to gamma-ray bursts.

Multigroup Radiation Magnetohydrodynamics Based on Discrete Ordinates including Compton Scattering

We present a formulation and numerical algorithm to extend the scheme for gray radiation magnetohydrodynamics (MHD) developed by Jiang to include the frequency dependence via the multigroup approach. The entire frequency space can be divided into an arbitrary number of groups in the lab frame, and we follow the time-dependent evolution of frequency-integrated specific intensities along discrete rays inside each group. Spatial transport of photons is done in the lab frame while all the coupling terms are solved in the fluid rest frame. Lorentz transformation is used to connect different frames. The radiation transport equation is solved fully implicitly in time while the MHD equations are evolved explicitly so that time step is not limited by the speed of light. A finite volume approach is used for transport in both spatial and frequency spaces to conserve the radiation energy density and momentum. The algorithm includes photon absorption, electron scattering, as well as Compton scattering, which is calculated by solving the Kompaneets equation. The algorithm is accurate for a wide range of optical depth conditions and can handle both radiation-pressure- and gas-pressure-dominated flows. It works for both Cartesian and curvilinear coordinate systems with adaptive mesh refinement. We provide a variety of test problems including a radiating sphere, shadow test, absorption of a moving gas, Bondi-type flows, as well as a collection of test problems for thermal and bulk Compton scattering. We also discuss examples where frequency dependence can make a big difference compared with the gray approach.

Integral Global Optimality Conditions and an Algorithm for Multiobjective Problems

In this work, we propose integral global optimality conditions for multiobjective problems not necessarily differentiable. The integral characterization, already known for single objective problems, are extended to multiobjective problems by weighted sum and Chebyshev weighted scalarizations. Using this last scalarization, we propose an algorithm for obtaining an approximation of the weak Pareto front whose effectiveness is illustrated by solving a collection of multiobjective test problems.

Improved Diagonal Hessian Approximations for Large-Scale Unconstrained Optimization

We consider some diagonal quasi-Newton methods for solving large-scale unconstrained optimization problems. A simple and effective approach for diagonal quasi-Newton algorithms is presented by proposing new updates of diagonal entries of the Hessian. Moreover, we suggest employing an extra BFGS update of the diagonal updating matrix and use its diagonal again. Numerical experiments on a collection of standard test problems show, in particular, that the proposed diagonal quasi-Newton methods perform substantially better than certain available diagonal methods.

Numerical approximation of modified Kawahara equation using Kernel smoothing method

In this article, a numerical approximation of modified Kawahara equation is investigated by Kernel smoothing method. The spatial derivatives involved in the modified Kawahara equation are approximated by smoothing Kernel method. Whereas, for the time integration, we employ Crank–Nicolson method. The conservative nature of the proposed scheme is demonstrated by the mass conservation constant (I1) and energy conservation constant (I2). To quantify the quality of the proposed scheme, we also have performed numerical testing on a collection of test problems.

Comparative assessment of smooth and non-smooth optimization solvers in HANSO software

The aim of this study is to compare the performance of smooth and nonsmooth optimization solvers from HANSO (Hybrid Algorithm for Nonsmooth Optimization) software. The smooth optimization solver is the implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and the nonsmooth optimization solver is the Hybrid Algorithm for Nonsmooth Optimization. More precisely, the nonsmooth optimization algorithm is the combination of the BFGS and the Gradient Sampling Algorithm (GSA). We use well-known collection of academic test problems for nonsmooth optimization containing both convex and nonconvex problems. The motivation for this research is the importance of the comparative assessment of smooth optimization methods for solving nonsmooth optimization problems. This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization problems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function evaluations and the number of subgradient evaluations are used to compare solvers.

Review and comparison of algorithms and software for mixed-integer derivative-free optimization

This paper reviews the literature on algorithms for solving bound-constrained mixed-integer derivative-free optimization problems and presents a systematic comparison of available implementations of these algorithms on a large collection of test problems. Thirteen derivative-free optimization solvers are compared using a test set of 267 problems. The testbed includes: (i) pure-integer and mixed-integer problems, and (ii) small, medium, and large problems covering a wide range of characteristics found in applications. We evaluate the solvers according to their ability to find a near-optimal solution, find the best solution among currently available solvers, and improve a given starting point. Computational results show that the ability of all these solvers to obtain good solutions diminishes with increasing problem size, but the solvers evaluated collectively found optimal solutions for 93% of the problems in our test set. The open-source solvers MISO and NOMAD were the best performers among all solvers tested. MISO outperformed all other solvers on large and binary problems, while NOMAD was the best performer on mixed-integer, non-binary discrete, small, and medium-sized problems.

An adaptive descent extension of the Polak–Rebière–Polyak conjugate gradient method based on the concept of maximum magnification

Recently, a one-parameter extension of the Polak–Rebiere–Polyak method has been suggested, having acceptable theoretical features and promising numerical behavior. Here, based on an eigenvalue analysis on the method with the aim of avoiding a search direction in the direction of the maximum magnification by a symmetric version of the search direction matrix, an adaptive formula for computing parameter of the method is proposed. Under standard assumptions, the given formula ensures the sufficient descent property and guarantees the global convergence of the method. Numerical experiments are done on a collection of CUTEr test problems. They show practical effectiveness of the suggested formula for the parameter of the method.

A sequential quadratic programming algorithm without a penalty function, a filter or a constraint qualification for inequality constrained optimization

This paper discusses a kind of nonlinear inequality constrained optimization problems without any constraint qualification. A new sequential quadratic programming algorithm for such problems is proposed, whose important features are as follows: (i) a new relaxation technique for the linearized constraints of the quadratic programming subproblem is introduced, which guarantees that the subproblem is always consistent and generates a favourable search direction; (ii) a weaker positive-definiteness assumption on the quadratic coefficient matrices is presented; (iii) a slightly new line search is adopted, where neither a penalty function nor a filter is used; (iv) an associated acceptable termination rule is introduced; (v) the finite convergence of the algorithm is proved. Furthermore, the numerical results on a collection of CUTE test problems show that the proposed algorithm is promising.

A collection of bivariate nonlinear optimisation test problems with graphical representations

Plotting objective functions, whenever possible, allows constraint activity, modality and convexity, as well as landscape features like basins, plateaus, valleys and saddle points, to be intuitively identified. Knowledge of these properties allow researchers to best select a set of functions for rating existing optimisation algorithms, when developing new search algorithms or for their control parameter tune-up. This paper offers a selection of 30 bivariate constrained objective functions accompanied by graphical representations. It is the first time a significant number of constrained-functions with their plots are gathered in one place. Also provided are the minima, maxima (global and local) and saddle point information of these functions, very useful in 'all optima' studies.

A collection of bivariate nonlinear optimisation test problems with graphical representations

Plotting objective functions, whenever possible, allows constraint activity, modality and convexity, as well as landscape features like basins, plateaus, valleys and saddle points, to be intuitively identified. Knowledge of these properties allow researchers to best select a set of functions for rating existing optimisation algorithms, when developing new search algorithms or for their control parameter tune-up. This paper offers a selection of 30 bivariate constrained objective functions accompanied by graphical representations. It is the first time a significant number of constrained-functions with their plots are gathered in one place. Also provided are the minima, maxima (global and local) and saddle point information of these functions, very useful in 'all optima' studies.

A conjugate gradient sampling method for nonsmooth optimization

We present an algorithm for minimizing locally Lipschitz functions being continuously differentiable in an open dense subset of $$\mathbb {R}^n$$. The function may be nonsmooth and/or nonconvex. The method makes use of a gradient sampling method along with a conjugate gradient scheme. To find search directions, we make use of a sequence of positive definite approximate Hessians based on conjugate gradient matrices. The algorithm benefits from a restart procedure to improve upon poor search directions or to make sure that the approximate Hessians remain bounded. The global convergence of the algorithm is established. An implementation of the algorithm is executed on a collection of well-known test problems. Comparative numerical results clearly show outperformance of the algorithm over some recent well-known nonsmooth algorithms using the Dolan–More performance profiles.

A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems

The field of continuous optimisation has witnessed an explosion of the so-called new or novel metaheuristic algorithms. Though not all of these algorithms are efficient as proclaimed by their inventors, a few of them have proved to be very efficient and thus have become popular tools for solving complex optimisation problems. Therefore, there is a need for a systematic analysis approach to fairly evaluate and compare the results of some of these optimisation algorithms. In this paper, a set of well-known mathematical benchmark functions are compiled to provide an easily accessible collection of standard benchmark test problems for continuous global optimisation. This set of test problems are used to investigate the computational capabilities and the microscopic behaviour of twelve different metaheuristic algorithms. The required number of function evaluations for reaching the best solution and the run-time complexity of the algorithms are compared. Furthermore, statistical tests are conducted to validate the concluding remarks.

High accuracy two-level implicit compact difference scheme for 1D unsteady biharmonic problem of first kind: application to the generalized Kuramoto–Sivashinsky equation

ABSTRACTWe propose a two-level implicit high order compact scheme for the one-dimensional unsteady biharmonic problem of first kind. The values of φ and are prescribed on the boundary. Using a combination of values of φ and at each grid point, the difference formula is derived for the unsteady biharmonic equation without discretizing the boundary conditions. The proposed method has second order time accuracy and fourth order space accuracy using just three grid points of a single compact stencil at every time level. The first order space derivative is also computed with same accuracy as a by-product of the method. Using the Von-Neumann analysis, the derived scheme is shown to be unconditionally stable. With a slight modification, the proposed method is applicable to solve singular problems. The performance of the proposed scheme is illustrated by numerical experiments done on a collection of test problems having physical significance including the nonlinear Kuramoto–Sivashinsky equation.

A New Dai-Liao Conjugate Gradient Method with Optimal Parameter Choice

A new nonlinear conjugate gradient method is proposed to solve large-scale unconstrained optimization problems. The direction is given by a search direction matrix, which contains a positive parameter. The value of the parameter is calculated by minimizing the upper bound of spectral condition number of the matrix defining it in order to cluster all the singular values. The new search direction satisfies the sufficient descent condition. Under some mild assumptions, the global convergence of the proposed method is proved for uniformly convex functions and the general functions. Numerical experiments show that, for the CUTEr library and the test problem collection given by Andrei, the proposed method is superior to M1 proposed by Babaie-Kafaki and Ghanbari (Eur. J. Oper. Res. 234(3), 625–630, 2014), CG_DESCENT(5.3), and CGOPT.

Scaled nonlinear conjugate gradient methods for nonlinear least squares problems

We propose a modified structured secant relation to get a more accurate approximation of the second curvature of the least squares objective function. Then, using this relation and an approach introduced by Andrei, we propose three scaled nonlinear conjugate gradient methods for nonlinear least squares problems. An attractive feature of one of the proposed methods is satisfication of the sufficient descent condition regardless of the line search and the objective function convexity. We establish that the three proposed algorithms are globally convergent, under the assumption of the Jacobian matrix having full column rank on the level set for one, and without such assumption for the other two. Numerical experiments are made on the collection of test problems, both zero-residual and nonzero-residual, using the Dolan–More performance profiles. They show that the outperformance of our proposed algorithms is more pronounced on nonzero-residual as well as large problems.