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Article
Announcement: Howard Rosenbrock Prize 2022
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Article
Hyperparameter autotuning of programs with HybridTuner
Algorithms must often be tailored to a specific architecture and application in order to fully harness the capabilities of sophisticated computer architectures and computational implementations. However, the r...
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Article
Branch-and-Model: a derivative-free global optimization algorithm
This paper presents a novel derivative-free global optimization algorithm Branch-and-Model (BAM). The BAM algorithm partitions the search domain dynamically, builds surrogate models around carefully selected eval...
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Article
Special Issue: Global Solution of Integer, Stochastic and Nonconvex Optimization Problems
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Article
SDP-quality bounds via convex quadratic relaxations for global optimization of mixed-integer quadratic programs
We consider the global optimization of nonconvex mixed-integer quadratic programs with linear equality constraints. In particular, we present a new class of convex quadratic relaxations which are derived via q...
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Article
Announcement: Howard Rosenbrock Prize 2021
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Article
Open AccessReview 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 algo...
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Article
Decomposition in derivative-free optimization
This paper proposes a novel decomposition framework for derivative-free optimization (DFO) algorithms. Our framework significantly extends the scope of current DFO solvers to larger-scale problems. We show tha...
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Article
Correction to: Announcement: Howard Rosenbrock Prize 2020
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Article
Announcement: Howard Rosenbrock Prize 2020
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Article
A triangulation and fill-reducing initialization procedure for the simplex algorithm
The computation of an initial basis is of great importance for simplex algorithms since it determines to a large extent the number of iterations and the computational effort needed to solve linear programs. We...
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Article
Backward Stepwise Elimination: Approximation Guarantee, a Batched GPU Algorithm, and Empirical Investigation
Best subset selection is NP-hard and expensive to solve exactly for problems with a large number of features. Practitioners often employ heuristics to quickly obtain approximate solutions without any accuracy ...
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Chapter and Conference Paper
HybridTuner: Tuning with Hybrid Derivative-Free Optimization Initialization Strategies
To utilize the full potential of advanced computer architectures, algorithms often need to be tuned to the architecture being used. We propose two hybrid derivative-free optimization (DFO) methods to maximize ...
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Article
Announcement: Howard Rosenbrock Prize 2019
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Article
Optimality-based domain reduction for inequality-constrained NLP and MINLP problems
In spatial branch-and-bound algorithms, optimality-based domain reduction is normally performed after solving a node and relies on duality information to reduce ranges of variables. In this work, we propose no...
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Article
On the impact of running intersection inequalities for globally solving polynomial optimization problems
We consider global optimization of nonconvex problems whose factorable reformulations contain a collection of multilinear equations of the form \(z_e = \prod _{v \in e} {z_v}\)ze=∏v∈ezv, \(e \in E\)e∈E, where E d...
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Article
Status report for optimization and engineering
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Article
Announcement: Howard Rosenbrock Prize 2018
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Article
Tuning BARON using derivative-free optimization algorithms
Optimization solvers include many options that allow users to control algorithmic aspects that may have a considerable impact on solver performance. Tuning solver options is often necessary to reduce execution...
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Article
Mixed-integer nonlinear programming 2018
Mixed-Integer Nonlinear Programming (MINLP) is the area of optimization that addresses nonlinear problems with continuous and integer variables. MINLP has proven to be a powerful tool for modeling. At the same...