We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. 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.

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CasADi: a software framework for nonlinear optimization and optimal control

Simultaneous optimization and solution methods for batch reactor control profiles

A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie)

Methods and software for derivative-based numerical optimization in general and simulation-based optimization in particular have seen a large rise in popularity over the past 30 years. Still, due to practical difficulties in implementing many of the methods in a fast and reliable manner, it remains an underused technology both in academia and in industry. To address this, we present a set of methods and tools with the aim of making techniques for dynamic optimization more accessible. In particular, we present CasADi, an open-source software framework for numerical optimization and algorithmic differentiation (AD) that offers a level of abstraction which is lower than algebraic modeling languages, but higher than conventional AD tools. We also discuss several of the many application problems which have already been addressed with CasADi by researchers from diverse fields.

https://lirias.kuleuven.be/bitstream/123456789/418048/1/thesis_final2.pdf

A General-Purpose Software Framework for Dynamic Optimization

Batch unit optimization with imperfect modelling: a survey

A near-optimal feedback control law for a distributed parameter system with a quadratic performance index is obtained by the method of trajectory approximation The system equation is reduced to an approximate system of ordinary differential equations by the Galerkin—Kantorovich method, and then Pontryagin's maximum principle is applied to show that the feedback control law is linear and can be obtained as the solution of a matrix Riccati equation Numerical computations performed using Chebyshev polynomials as the Galerkin weighting functions on the equation for a heat exchanger with wall flux forcing indicate that four thermocouples are enough to attain virtual optimality

L. L. Lynn

Papers

The use of orthogonal polynomials in the near-optimal control of distributed systems by trajectory approximation†