With experimental data from tests in outdoor environment (~10000 timesteps), I checked that simulation with cvode was about 2x faster than ode() and was happy with that. “OpenJDK 64-Bit Server VM (build 25.292-b10, mixed mode)” “OpenJDK Runtime Environment (build 1.8.0_292-b10)” Start Fmincon 1.0.4: loading macros, help, demos. Start SciColPack: load macros, dependencies, gateways, help, demos. Start SciIPOpt 1.3.2: load gateways, help, demos.

I installed manually from sources fmincon1.0.4 because of failures with recent 1.0.5 or former 1.0.3 compiled build from ATOMS with Scilab_6.1.2_Sundial build. In particular, it is about combined use of Scilab_6.1.2_Sundial::cvode() and atoms module fmincon with dependencies sci-ipopt and scicolpack. I’m sharing feedback for discussion around these new powerful features (solvers) since I’ve been playing around the last releases, from previous discussion. This is the occasion of thinking about revamping the differential equations module, which could embrace theses new solvers and the actual ones, in a unified API, which could also propose new solvers. The underlying Sundials library also allows to consider large scale systems of stiff equations (typically coming from discretization of partial differential equations) by specifying sparse Jacobians or sparsity patterns allowing efficient estimation thereof. The help pages (available online) and demos should give you a good idea of the user-friendly features that are available, which can be compared to the actual features of Scilab solvers such as ode() and dassl(). Preview builds of Scilab allowing to use all the solvers and their features are available at (look for “Sundials/Colpack preview builds”). They are mainly about differential equations, differential-algebraic and non-linear systems of equations and their solvers as implemented by the recently awarded SUNDIALS solver suite. We would like to have your impressions about forthcoming features.

Scilab suite