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Constrained multiobjective optimization of expensive black-box functions using a heuristic branch-and-bound approach
While constrained, multiobjective optimization is generally very difficult, there is a special case in which such problems can be solved with a...
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Hybrid limited memory gradient projection methods for box-constrained optimization problems
Gradient projection methods represent effective tools for solving large-scale constrained optimization problems thanks to their simple implementation...
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Convergence of derivative-free nonmonotone Direct Search Methods for unconstrained and box-constrained mixed-integer optimization
This paper presents a class of nonmonotone Direct Search Methods that converge to stationary points of unconstrained and boxed constrained...
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DEFT-FUNNEL: an open-source global optimization solver for constrained grey-box and black-box problems
The fast-growing need for grey-box and black-box optimization methods for constrained global optimization problems in fields such as medicine,...
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Dependence in constrained Bayesian optimization
Constrained Bayesian optimization optimizes a black-box objective function subject to black-box constraints. For simplicity, most existing works...
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The exact projective penalty method for constrained optimization
A new exact projective penalty method is proposed for the equivalent reduction of constrained optimization problems to nonsmooth unconstrained ones....
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Adaptive Global Algorithm for Solving Box-Constrained Non-convex Quadratic Minimization Problems
In this paper, we propose a new adaptive method for solving the non-convex quadratic minimization problem subject to box constraints, where the...
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Preconditioning of discrete state- and control-constrained optimal control convection-diffusion problems
We consider the iterative solution of algebraic systems, arising in optimal control problems constrained by a partial differential equation with...
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Design of the Best Linear Classifier for Box-Constrained Data Sets
We construct a binary linear classifier for n-dimensional data sets with the special box-constrained structure. Data sets with this structure arise...
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Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization
The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the...
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On complexity and convergence of high-order coordinate descent algorithms for smooth nonconvex box-constrained minimization
Coordinate descent methods have considerable impact in global optimization because global (or, at least, almost global) minimization is affordable...
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Combined Newton-Gradient Method for Constrained Root-Finding in Chemical Reaction Networks
In this work, we present a fast, globally convergent, iterative algorithm for computing the asymptotically stable states of nonlinear large-scale...
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Learning Enabled Constrained Black-Box Optimization
This chapter looks at the issue of black-box constrained optimization where both the objective function and the constraints are unknown and can only...
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Extending oscars-ii to generally constrained global optimization
A derivative free method for generally constrained global optimization is described. A non-smooth merit function with one parameter is used. When...
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A Boosted-DCA with Power-Sum-DC Decomposition for Linearly Constrained Polynomial Programs
This paper proposes a novel Difference-of-Convex (DC) decomposition for polynomials using a power-sum representation, achieved by solving a sparse...
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Constrained many-to-many point matching in two dimensions
In the minimum-weight many-to-many point matching problem, we are given a set R of red points and a set B of blue points in the plane, of total size N ...
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Derivative-free methods for mixed-integer nonsmooth constrained optimization
In this paper, mixed-integer nonsmooth constrained optimization problems are considered, where objective/constraint functions are available only as...
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An Efficient Hessian Based Algorithm for Singly Linearly and Box Constrained Least Squares Regression
The singly linearly and box constrained least squares regression has diverse applications in various fields. This paper builds upon previous work to...
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Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization
Black-box surrogate-based optimization has received increasing attention due to the growing interest in solving optimization problems with embedded...
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A taxonomy of constraints in black-box simulation-based optimization
The types of constraints encountered in black-box simulation-based optimization problems differ significantly from those addressed in nonlinear...
