Trending Research
Escaping local minima with derivative-free methods: a numerical investigation
We apply a state-of-the-art, local derivative-free solver, Py-BOBYQA, to global optimization problems, and propose an algorithmic improvement that is beneficial in this context.
Optimization and Control
Universal Average-Case Optimality of Polyak Momentum
d2l-ai/d2l-en
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Polyak momentum (PM), also known as the heavy-ball method, is a widely used optimization method that enjoys an asymptotic optimal worst-case complexity on quadratic objectives.
Optimization and Control
Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with acados
The recently proposed zero-order robust optimization (zoRO) algorithm mitigates the computational cost of uncertainty-aware MPC by propagating the uncertainties outside of the MPC problem.
Optimization and Control
Computing in Operations Research using Julia
The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome low-level languages such as C, C++, and Fortran and highly expressive yet typically slow high-level languages such as Python and MATLAB.
Optimization and Control Numerical Analysis Programming Languages
A spectral-Galerkin turbulent channel flow solver for large-scale simulations
A fully (pseudo-)spectral solver for direct numerical simulations of large-scale turbulent channel flows is described.
Numerical Analysis Numerical Analysis Fluid Dynamics
Distributed Continuous-Time Optimization with Time-Varying Objective Functions and Inequality Constraints
Different from most studied distributed optimization problems with time-invariant objective functions and constraints, the optimal solution in this paper is time varying and forms a trajectory.
Optimization and Control
EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics
To demonstrate the potential of the approach, we address the identification of a linear non-autonomous system consisting of a shear building model excited by real seismograms, and the identification of a partially observed nonlinear system.
Dynamical Systems Systems and Control Systems and Control
Adaptive Deep Fourier Residual method via overlapping domain decomposition
mathmode/adaptive-deep-fourier-residual-method-via-overlapping-domain-decomposition
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The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural networks (VPINNs).
Numerical Analysis Numerical Analysis
Neural-network learning of SPOD latent dynamics
The proposed method is based on three fundamental steps: in the first, once that the mean flow field has been subtracted from the realizations (also referred to as snapshots), we compress the data from a high-dimensional representation to a lower dimensional one by constructing the SPOD latent space; in the second, we build the time-dependent coefficients by projecting the snapshots containing the fluctuations onto the SPOD basis and we learn their evolution in time with the aid of recurrent neural networks; in the third, we reconstruct the high-dimensional data from the learnt lower-dimensional representation.
Numerical Analysis Numerical Analysis
Frequency Regulation with Storage: On Losses and Profits
Thanks to this constraint, the amount of electricity traded on day-ahead markets is an implicit function of the regulation power sold to grid operators.
Optimization and Control Systems and Control General Economics Systems and Control Economics
