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The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory; however, it still faces the challenges of large deviation from the optimal solution and inability to satisfy the specific flight time and fuel mass constraints. In this paper, a modified shape approximation low-thrust model is presented, and a novel constrained optimization algorithm is developed to solve this problem. The proposed method aims at settling the bi-objective optimization orbit involving the twin objectives of minimum flight time and low fuel consumption and enhancing the accuracy of optimized orbit. In particular, a transformed high-order polynomial model based on finite Fourier series is proposed, which can be characterized as a multi-constraint optimization problem. Then, a novel optimization algorithm is specifically developed to optimize the large-scale multi-constraint dynamical equations of shape trajectory. The key performance indicators of the index include minimum flight time, low fuel consumption and bi-objective optimization of the two. Simulation results prove that this approach possesses both the high precision achievable by numerical methods and low computational complexity offered by shape approximation techniques. Besides, the Pareto front of the fuel-time bi-objective optimization orbit is firstly introduced to analyze an intact optimal solution set. Furthermore, we have demonstrated that our proposed approach is appropriate to generate the preliminary orbit for pseudo-spectral method.

In this contribution we propose and rigorously analyze new variants of adaptive Trust-Region methods for parameter optimization with PDE constraints and bilateral parameter constraints. The approach employs successively enriched Reduced Basis surrogate models that are constructed during the outer optimization loop and used as model function for the Trust-Region method. Each Trust-Region sub-problem is solved with the projected BFGS method. Moreover, we propose a non-conforming dual (NCD) approach to improve the standard RB approximation of the optimality system. Rigorous improved a posteriori error bounds are derived and used to prove convergence of the resulting NCD-corrected adaptive Trust-Region Reduced Basis algorithm. Numerical experiments demonstrate that this approach enables to reduce the computational demand for large scale or multi-scale PDE constrained optimization problems significantly.

In this paper, a new take on the concept of an active subspace for reducing the dimension of the design parameter space in a multidisciplinary analysis and optimization (MDAO) problem is proposed. The new approach is intertwined with the concepts of adaptive parameter sampling, projection-based model order reduction, and a database of linear, projection-based reduced-order models equipped with interpolation on matrix manifolds, in order to construct an efficient computational framework for MDAO. The framework is fully developed for MDAO problems with linearized fluid-structure interaction constraints. It is applied to the aeroelastic tailoring, under flutter constraints, of two different flight systems: a flexible configuration of NASA's Common Research Model; and NASA's Aeroelastic Research Wing #2 (ARW-2). The obtained results illustrate the feasibility of the computational framework for realistic MDAO problems and highlight the benefits of the new approach for constructing an active subspace in both terms of solution optimality and wall-clock time reduction

We propose a local regularization of elliptic optimal control problems which involves the nonconvex $$L^q$$ quasi-norm penalization in the cost function. The proposed Huber type regularization allows us to formulate the PDE constrained optimization instance as a DC programming problem (difference of convex functions) that is useful to obtain necessary optimality conditions and tackle its numerical solution by applying the well known DC algorithm used in nonconvex optimization problems. By this procedure we approximate the original problem in terms of a consistent family of parameterized nonsmooth problems for which there are efficient numerical methods available. Finally, we present numerical experiments to illustrate our theory with different configurations associated to the parameters of the problem.

This paper considers nonconvex distributed constrained optimization over networks, modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic framework for the minimization of the sum of a smooth nonconvex (nonseparable) function--the agent's sum-utility--plus a Difference-of-Convex (DC) function (with nonsmooth convex part). This general formulation arises in many applications, from statistical machine learning to engineering. The proposed distributed method combines successive convex approximation techniques with a judiciously designed perturbed push-sum consensus mechanism that aims to track locally the gradient of the (smooth part of the) sum-utility. Sublinear convergence rate is proved when a fixed step-size (possibly different among the agents) is employed whereas asymptotic convergence to stationary solutions is proved using a diminishing step-size. Numerical results show that our algorithms compare favorably with current schemes on both convex and nonconvex problems., Comment: Submitted June 3, 2017, revised June 5, 2108. Part of this work has been presented at the 2016 Asilomar Conference on System, Signal and Computers and the 2017 IEEE ICASSP Conference

En la resolución de los problemas de optimización con restricciones (también conocida con el nombre de programación matemática) se pueden establecer diversidad de algoritmos. Como la investigación en este campo es muy amplia, continuamente se están desarrollando nuevos métodos, cada vez más sofisticados, que permiten resolver este tipo de problemas. En este artículo se presentarán los métodos de penalización. Estos son los más intuitivos y permiten mostrar una introducción dentro de la resolución de este tipo de problemas. Se estudiarán sus propiedades y, luego, se procederá a su deducción, interpretación y demostración de su convergencia. Finalmente, se presentará el método del Lagrangiano aumentado. Este es un método que mejora a los anteriores y con una mayor velocidad de convergencia. Así mismo, este artículo supone una introducción a los algoritmos de resolución de problemas de optimización con restricciones, mostrando una iniciación a los métodos numéricos empleados en la programación matemática.

In many engineering applications, it is necessary to minimize smooth functions plus penalty (or regularization) terms that violate smoothness and convexity. Specific algorithms for this type of problems are available in recent literature. Here, a smooth reformulation is analyzed and equivalence with the original problem is proved both from the points of view of global and local optimization. Moreover, for the cases in which the objective function is much more expensive than the constraints, model-intensive algorithms, accompanied by their convergence and complexity theories, are introduced. Finally, numerical experiments are presented.

Many modern real-valued optimization tasks use &ldquo, black-box&rdquo, (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cLSGO) tasks. Today, the IEEE Congress of Evolutionary Computation provides a special session and several benchmarks for LSGO. At the same time, cLSGO problems are not well studied yet. The majority of modern optimization techniques demonstrate insufficient performance when confronted with cLSGO tasks. The effectiveness of evolution algorithms (EAs) in solving constrained low-dimensional optimization problems has been proven in many scientific papers and studies. Moreover, the cooperative coevolution (CC) framework has been successfully applied for EA used to solve LSGO problems. In this paper, a new approach for solving cLSGO has been proposed. This approach is based on CC and a method that increases the size of groups of variables at the decomposition stage (iCC) when solving cLSGO tasks. A new algorithm has been proposed, which combined the success-history based parameter adaptation for differential evolution (SHADE) optimizer, iCC, and the &epsilon, constrained method (namely &epsilon, iCC-SHADE). We investigated the performance of the &epsilon, iCC-SHADE and compared it with the previously proposed &epsilon, CC-SHADE algorithm on scalable problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization.

Model-reduction techniques aim to reduce the computational complexity of simulating dynamical systems by applying a (Petrov-)Galerkin projection process that enforces the dynamics to evolve in a low-dimensional subspace of the original state space. Frequently, the resulting reduced-order model (ROM) violates intrinsic physical properties of the original full-order model (FOM) (e.g., global conservation, Lagrangian structure, state-variable bounds) because the projection process does not generally ensure preservation of these properties. However, in many applications, ensuring the ROM preserves such intrinsic properties can enable the ROM to retain physical meaning and lead to improved accuracy and stability properties. In this work, we present a general constrained-optimization formulation for projection-based model reduction that can be used as a template to enforce the ROM to satisfy specific properties on the kinematics and dynamics. We introduce constrained-optimization formulations at both the time-continuous (i.e., ODE) level, which leads to a constrained Galerkin projection, and at the time-discrete level, which leads to a least-squares Petrov-Galerkin (LSPG) projection, in the context of linear multistep schemes. We demonstrate the ability of the proposed formulations to equip ROMs with desired properties such as global energy conservation and bounds on the total variation.

A methodology is proposed for the efficient solution of probabilistic nonconvex constrained optimization problems with uncertain. Statistical properties of the underlying stochastic generator are characterized from an initial statistical sample of function evaluations. A diffusion manifold over the initial set of data points is first identified and an associated basis computed. The joint probability density function of this initial set is estimated using a kernel density model and an Ito stochastic differential equation constructed with this model as its invariant measure. This ISDE is adapted to fluctuate around the manifold yielding additional joint realizations of the uncertain parameters, design variables, and function values are obtained as solutions of the ISDE. The expectations in the objective function and constraints are then accurately evaluated without performing additional function evaluations. The methodology brings together novel ideas from manifold learning and stochastic Hamiltonian dynamics to tackle an outstanding challenge in stochastic optimization. Three examples are presented to highlight different aspects of the proposed methodology.

Solving a multi-input dynamic optimization of a batch processes is a complex problem involving interactions between input variables and constraints over time. The problem gets more difficult due to the presence of local solutions that have almost the same cost but widely varying structures. This paper studies various local optimal solutions for a non-isothermal semi-batch reactor with the feed rate and temperature as inputs and a heat removal constraint. Three solution patterns were studied, all consisting in meeting the heat removal constraint for the first part and seeking the compromise between the main and side reactions in the later part. A sensitivity analysis shows that the best solution pattern among those studied does not change with variations in parameters or initial conditions. (C) 2016 Elsevier Ltd. All rights reserved.

Quantum annealers such as D-Wave machines are designed to propose solutions for quadratic unconstrained binary optimization (QUBO) problems by mapping them onto the quantum processing unit, which tries to find a solution by measuring the parameters of a minimum-energy state of the quantum system. While many NP-hard problems can be easily formulated as binary quadratic optimization problems, such formulations almost always contain one or more constraints, which are not allowed in a QUBO. Embedding such constraints as quadratic penalties is the standard approach for addressing this issue, but it has drawbacks such as the introduction of large coefficients and using too many additional qubits. In this paper, we propose an alternative approach for implementing constraints based on a combinatorial design and solving mixed-integer linear programming (MILP) problems in order to find better embeddings of constraints of the type &sum, x i = k for binary variables x i. Our approach is scalable to any number of variables and uses a linear number of ancillary variables for a fixed k.

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Ito stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter {\epsilon}. Currently, {\epsilon} is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of {\epsilon}, based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases., Comment: Co author is not happy with the paper would like to withdraw submission and improve the paper

The aim of the present work is to find a solution to non-linear constrained optimization problems of structure. Constrained optimization difficulties are practical shortcomings. The loopholes of traditional numerical methods are being removed by heuristic methods as no requirement of the functional derivatives is desired and approaches to the global way out. This article presents a “penalty guided Accelerated Particle Swarm Optimization (APSO) algorithm” to search the problem’s optimal solution in the feasible region of whole search domain. There is numerical result and comparison of the structural design optimization problems. The way out by the current perspective proves to be the better than other techniques and it can be said that our findings show better solutions to engineering problems than those earlier obtained using current algorithms.

We present an abstract mathematical framework for an optimization-based additive decomposition of a large class of variational problems into a collection of concurrent subproblems. The framework replaces a given monolithic problem by an equivalent constrained optimization formulation in which the subproblems define the optimization constraints and the objective is to minimize the mismatch between their solutions. The significance of this reformulation stems from the fact that one can solve the resulting optimality system by an iterative process involving only solutions of the subproblems. Consequently, assuming that stable numerical methods and efficient solvers are available for every subproblem, our reformulation leads to robust and efficient numerical algorithms for a given monolithic problem by breaking it into subproblems that can be handled more easily. An application of the framework to the Oseen equations illustrates its potential.

Summary An adaptive approach to using reduced-order models (ROMs) as surrogates in partial differential equations (PDE)-constrained optimization is introduced that breaks the traditional offline-online framework of model order reduction. A sequence of optimization problems constrained by a given ROM is defined with the goal of converging to the solution of a given PDE-constrained optimization problem. For each reduced optimization problem, the constraining ROM is trained from sampling the high-dimensional model (HDM) at the solution of some of the previous problems in the sequence. The reduced optimization problems are equipped with a nonlinear trust-region based on a residual error indicator to keep the optimization trajectory in a region of the parameter space where the ROM is accurate. A technique for incorporating sensitivities into a reduced-order basis is also presented, along with a methodology for computing sensitivities of the ROM that minimizes the distance to the corresponding HDM sensitivity, in a suitable norm. The proposed reduced optimization framework is applied to subsonic aerodynamic shape optimization and shown to reduce the number of queries to the HDM by a factor of 4-5, compared with the optimization problem solved using only the HDM, with errors in the optimal solution far less than 0.1%. Copyright © 2014 John Wiley & Sons, Ltd.

We present a new optimization-based method for atomistic-to-continuum (AtC) coupling. The main idea is to cast the latter as a constrained optimization problem with virtual Dirichlet controls on the interfaces between the atomistic and continuum subdomains. The optimization objective is to minimize the error between the atomistic and continuum solutions on the overlap between the two subdomains, while the atomistic and continuum force balance equations provide the constraints. Separation, rather then blending of the atomistic and continuum problems, and their subsequent use as constraints in the optimization problem distinguishes our approach from the existing AtC formulations. We present and analyze the method in the context of a one-dimensional chain of atoms modeled using a linearized two-body potential with next-nearest neighbor interactions.

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by \cite{wen2013feasible} for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform for future methodology developments along this line of work.

Includes erratum The broad class of extended real-valued lower semicontinuous (lsc) functions on IRn captures nearly all functions of practical importance in equation solving, variational problems, fitting, and estimation. The paper develops piecewise polynomial functions, called epi-splines, that approximate any lsc function to an arbitrary level of accuracy. Epi-splines provide the foundation for the solution of a rich class of function identification problems that incorporate general constraints on the function to be identified including those derived from information about smoothness, shape, proximity to other functions, and so on. As such extrinsic information as well as observed function and subgradient values often evolve in applications, we establish conditions under which the computed epi-splines converge to the function we seek to identify. Numerical examples in response surface building and probability density estimation illustrate the framework. U. S. Army Research Laboratory and the U. S. Army Research Office grant 00101-80683 U. S. Army Research Laboratory and the U. S. Army Research Office grant W911NF-10-1-0246 U. S. Army Research Laboratory and the U. S. Army Research Office grant W911NF-12-1-0273 U. S. Army Research Laboratory and the U. S. Army Research Office grant 00101-80683 U. S. Army Research Laboratory and the U. S. Army Research Office grant W911NF-10-1-0246 U. S. Army Research Laboratory and the U. S. Army Research Office grant W911NF-12-1-0273

Flows in fractured media have been modeled with many different approaches in order to get reliable and efficient simulations for many critical applications. The common issues to be tackled are the wide range of scales involved in the phenomenon, the complexity of the domain, and the huge computational cost. In this paper we introduce residual-based “a posteriori” error estimates for a formulation of the flow in the discrete fracture networks based on a constrained optimization approach (see Berrone, Pieraccini, and Scialo [SIAM J. Sci. Comput., 35 (2013), pp. B487--B510], [SIAM J. Sci. Comput., 35 (2013), pp. A908--A935], [J. Comput. Phys., 256 (2014), pp. 838--853]), suitable to overcome all the difficulties related to a good quality mesh generation with conformity requirement.

An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task (MT) Bayesian compressive sensing (BCS) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine (RVM) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT-BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods.

The widely used estimator of Berry, Levinsohn, and Pakes (1995 )p roduces estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks, and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP’s estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking demand models where the Bellman equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization. For static BLP, the constrained optimization approach can be as much as ten to forty times faster for large-dimensional problems with many markets.

This work is concerned with the cyclic block coordinate descent method, or nonlinear Gauss-Seidel method, where the solution of an optimization problem is achieved by partitioning the variables in blocks and successively minimizing with respect to each block. The properties of the objective function that guarantee the convergence of such alternating scheme have been widely investigated in the literature and it is well known that, without suitable convexity hypotheses, the method may fail to locate the stationary points when more than two blocks of variables are employed. In this paper the general constrained non convex case is considered and three contributions are given. First, a general method allowing an approximate solution of each block minimization subproblem is devised and the related convergence analysis is developed, showing that the proposed inexact method has the same convergence properties of the standard nonlinear Gauss-Seidel method. Then, a cyclic block gradient projection method is analyzed, proving that it leads to stationary points for every number of blocks. Finally, the cyclic block gradient method is applied to large scale problems arising from the nonnegative matrix factorization approach. The results of a numerical experimentation on image recognition problems are also reported.

In this paper we investigate the possibility of using a block triangular preconditioner for saddle point problems arising in PDE constrained optimization. In particular we focus on a conjugate gradient-type method introduced by Bramble and Pasciak which uses self adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method – the appropriate scaling of the preconditioners – is easily overcome. We present an eigenvalue analysis for the block triangular preconditioners which gives convergence bounds in the non-standard inner product and illustrate their competitiveness on a number of computed examples.

Topology optimization of continuum structures is a relatively new branch of the structural optimization field. Since the basic principles were first proposed by Bendsøe and Kikuchi in 1988, most of the work has been dedicated to the so-called maximum stiffness (or minimum compliance) formulations. However, since a few years different approaches have been proposed in terms of minimum weight with stress (and/or displacement) constraints.These formulations give rise to more complex mathematical programming problems, since a large number of highly non-linear (local) constraints must be taken into account. In an attempt to reduce the computational requirements, in this paper, we propose different alternatives to consider stress constraints and some ideas about the numerical implementation of these algorithms. Finally, we present some application examples.

In this paper, a simple feasible SQP method for nonlinear inequality constrained optimization is presented. At each iteration, we need to solve one QP subproblem only. After solving a system of linear equations, a new feasible descent direction is designed. The Maratos effect is avoided by using a high-order corrected direction. Under some suitable conditions the global and superlinear convergence can be induced. In the end, numerical experiments show that the method in this paper is effective.

We investigate a linear iteration scheme for solving Karush-Kuhn-Tucker systems arising from optimization problems with linear equality constraints. The iterations are motivated by the simplicity of the proposed combination of iterations for the forward and adjoint systems that need to be solved and for which efficient solvers may already be available. Convergence results are derived, and their practical relevance is investigated by means of a numerical example.

In this paper we present a new method for solving the Stokes problem which is a constrained optimization method. The new method is simpler and requires less computation than the existing methods. In this method we transform the Stokes problem into a quadratic programming problem and by solving it, the velocity and the pressure are obtained.

Numerous scientific applications across a variety of fields depend on box-constrained convex optimization. Box-constrained problems therefore continue to attract research interest. We address box-constrained (strictly convex) problems by deriving two new quasi-Newton algorithms. Our algorithms are positioned between the projected-gradient [J. B. Rosen, J. SIAM, 8 (1960), pp. 181–217] and projected-Newton [D. P. Bertsekas, SIAM J. Control Optim., 20 (1982), pp. 221–246] methods. We also prove their convergence under a simple Armijo step-size rule. We provide experimental results for two particular box-constrained problems: nonnegative least squares (NNLS), and nonnegative Kullback–Leibler (NNKL) minimization. For both NNLS and NNKL our algorithms perform competitively as compared to well-established methods on medium-sized problems; for larger problems our approach frequently outperforms the competition.

Numerical test results are presented for solving smooth nonlinear programming problems with a large number of constraints, but a moderate number of variables. The active set method proceeds from a given bound for the maximum number of expected active constraints at an optimal solution, which must be less than the total number of constraints. A quadratic programming subproblem is generated with a reduced number of linear constraints from the so-called working set, which is internally changed from one iterate to the next. Only for active constraints, i.e., a certain subset of the working set, new gradient values must be computed. The line search is adapted to avoid too many active constraints which do not fit into the working set. The active set strategy is an extension of an algorithm described earlier by the author together with a rigorous convergence proof. Numerical results for some simple academic test problems show that nonlinear programs with up to 200,000,000 nonlinear constraints are efficiently solved on a standard PC.

In a posteriori error analysis of reduced basis approximations to affinely parametrized partial differential equations, the construction of lower bounds for the coercivity and inf-sup stability constants is essential. In (Huynh et al., C. R. Acad. Sci. Paris Ser. I Math. 345 (2007) 473-478), the authors presented an efficient method, compatible with an off-line/on-line strategy, where the on- line computation is reduced to minimizing a linear functional under a few linear constraints. These constraints depend on nested sets of parameters obtained iteratively using a greedy algorithm. We improve here this method so that it becomes more efficient and robust due to two related properties: (i) the lower bound is obtained by a monotonic process with respect to the size of the nested sets; (ii) less eigen-problems need to be solved. This improved evaluation of the inf-sup constant is then used to consider a reduced basis approximation of a parameter dependent electromagnetic cavity problem both for the greedy construction of the elements of the basis and the subsequent validation of the reduced basis approximation. The problem we consider has resonance features for some choices of the parameters that are well captured by the methodology.