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Implementation of an oracle-structured bundle method for distributed optimization
We consider the problem of minimizing a function that is a sum of convex agent functions plus a convex common public function that couples them. The...
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A Partially Inertial Customized Douglas–Rachford Splitting Method for a Class of Structured Optimization Problems
In this paper, we are concerned with a class of structured optimization problems frequently arising from image processing and statistical learning,...
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Sparse conic reformulation of structured QCQPs based on copositive optimization with applications in stochastic optimization
Recently, Bomze et al. introduced a sparse conic relaxation of the scenario problem of a two stage stochastic version of the standard quadratic...
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Smooth over-parameterized solvers for non-smooth structured optimization
Non-smooth optimization is a core ingredient of many imaging or machine learning pipelines. Non-smoothness encodes structural constraints on the...
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Structured Low-Rank Approximation: Optimization on Matrix Manifold Approach
We deal with the problem to compute the nearest Structured Low-Rank Approximation (SLRA) to a given matrix in this paper. This problem arises in many...
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An augmented Lagrangian method for optimization problems with structured geometric constraints
This paper is devoted to the theoretical and numerical investigation of an augmented Lagrangian method for the solution of optimization problems with...
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Accelerated gradient sliding for structured convex optimization
Our main goal in this paper is to show that one can skip gradient computations for gradient descent type methods applied to certain structured convex...
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Nonnegative Matrices and Their Structured Singular Values
AbstractIn this article, we present new results for the computation of structured singular values of nonnegative matrices subject to pure complex...
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Structured interpolation for multivariate transfer functions of quadratic-bilinear systems
High-dimensional/high-fidelity nonlinear dynamical systems appear naturally when the goal is to accurately model real-world phenomena. Many physical...
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Minimizing oracle-structured composite functions
We consider the problem of minimizing a composite convex function with two different access methods: an oracle , for which we can evaluate the value...
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Structured tensor tuples to polynomial complementarity problems
It is well known that structured tensors play an important role in the investigation of tensor complementarity problems. The polynomial...
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Structured Products with Dynamic Asset Allocation and Systematic Strategies
The structured solutions presented so far are mostly option-based payoffs on traditional asset classes. In this chapter, we introduce the structured...
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Automatic Block-Structured Grid Generation in Turbo Machine Blade Passages by TurboR&D.Mesher Software
AbstractThis article describes the methodology of generating block-structured computational grids for turbomachine blade passages implemented in the...
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Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
An essential tool in data-driven modeling of dynamical systems from frequency response measurements is the barycentric form of the underlying...
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Structured adaptive spectral-based algorithms for nonlinear least squares problems with robotic arm modelling applications
This research article develops two adaptive, efficient, structured non-linear least-squares algorithms, NLS. The approach taken to formulate these...
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Optimal methods for convex nested stochastic composite optimization
Recently, convex nested stochastic composite optimization (NSCO) has received considerable interest for its applications in reinforcement learning...
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Dominant subspaces of high-fidelity polynomial structured parametric dynamical systems and model reduction
In this work, we investigate a model order reduction scheme for high-fidelity nonlinear structured parametric dynamical systems. More specifically,...
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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...
