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A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie)
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TLDR
CasADi is presented, an open-source software framework for numerical optimization and algorithmic differentiation that offers a level of abstraction which is lower than algebraic modeling languages, but higher than conventional AD tools.About:
The article was published on 2013-10-24 and is currently open access. It has received 283 citations till now.read more
Citations
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CasADi: a software framework for nonlinear optimization and optimal control
TL;DR: This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.
Posted Content
Neural Ordinary Differential Equations
TL;DR: In this paper, the authors introduce a new family of deep neural network models called continuous normalizing flows, which parameterize the derivative of the hidden state using a neural network, and the output of the network is computed using a black-box differential equation solver.
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JuMP: A Modeling Language for Mathematical Optimization
TL;DR: JuMP as mentioned in this paper is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax.
Journal ArticleDOI
Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
TL;DR: A novel modeling framework for forecasting electricity prices is proposed and it is shown how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant.
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Cautious Model Predictive Control Using Gaussian Process Regression
TL;DR: This work describes a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control and presents a model predictive control approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP.
References
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Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Book
Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Journal ArticleDOI
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
Book
Mathematical Theory of Optimal Processes
TL;DR: The fourth and final volume in this comprehensive set presents the maximum principle as a wide ranging solution to nonclassical, variational problems as discussed by the authors, which can be applied in a variety of situations, including linear equations with variable coefficients.