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A gradient-based bilevel optimization approach for tuning regularization hyperparameters
Hyperparameter tuning in the area of machine learning is often achieved using naive techniques, such as random search and grid search. However, most...
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A Primal-dual Backward Reflected Forward Splitting Algorithm for Structured Monotone Inclusions
We propose a primal-dual backward reflected forward splitting method for solving structured primal-dual monotone inclusions in real Hilbert spaces....
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Global optimization via optimal decision trees
The global optimization literature places large emphasis on reducing intractable optimization problems into more tractable structured optimization...
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Convex Predictor–Nonconvex Corrector Optimization Strategy with Application to Signal Decomposition
Many tasks in real life scenarios can be naturally formulated as nonconvex optimization problems. Unfortunately, to date, the iterative numerical...
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A hierarchically low-rank optimal transport dissimilarity measure for structured data
We develop a class of hierarchically low-rank, scalable optimal transport dissimilarity measures for structured data, bringing the current...
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Determining optimal channel partition for 2:4 fine grained structured sparsity
Deep Neural Networks (DNNs) have demonstrated tremendous success in many applications, but incur high computational burden on the inference side. The...
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A unifying framework for tangential interpolation of structured bilinear control systems
In this paper, we consider the structure-preserving model order reduction problem for multi-input/multi-output bilinear control systems by tangential...
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Solving Optimization Problems with the Heuristic Kalman Algorithm New Stochastic Methods
This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering...
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Level constrained first order methods for function constrained optimization
We present a new feasible proximal gradient method for constrained optimization where both the objective and constraint functions are given by...
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Hermite least squares optimization: a modification of BOBYQA for optimization with limited derivative information
Derivative-free optimization tackles problems, where the derivatives of the objective function are unknown. However, in practical optimization...
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Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine...
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Optimal Contraception Control Problems in a Nonlinear Size-Structured Vermin Model
This paper investigates a size-structured vermin contraception control model with nonlinear fertility and mortality, in which the control variable...
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Block-Structured Quad Meshing for Supersonic Flow Simulations
Quad meshing is a very well-studied domain for many years. While the problem can be globally considered as solved, many approaches do not provide...
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Sequential M-Stationarity Conditions for General Optimization Problems
In this paper, we investigate sequential M-stationarity conditions for a class of nonsmooth nonconvex general optimization problems. We introduce...
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Compact representations of structured BFGS matrices
For general large-scale optimization problems compact representations exist in which recursive quasi-Newton update formulas are represented as...
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Artificial Intelligence Problems and Combinatorial Optimization
The method of modeling artificial intelligence problems using the theory of combinatorial optimization is described. As a result of these studies,...
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SoRoTop : a hitchhiker’s guide to topology optimization MATLAB code for design-dependent pneumatic-driven soft robotsDemands for pneumatic-driven soft robots are constantly rising for various applications. However, they are often designed manually due to the lack of...
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A Proximal Alternating Direction Method of Multipliers for DC Programming with Structured Constraints
In this paper, we consider a class of structured DC programming, where the objective function is the difference of two (possibly nonsmooth) convex...
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Parallel 3D topology optimization with multiple constraints and objectives
This paper introduces a parallel Topology Optimization (TO) platform capable of optimizing designs for multiple objectives, whilst subject to...
