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The MATLAB/DIDO toolbox does not require a "guess" to run the algorithm. This and other distinguishing features have made DIDO a popular tool to solve optimal control problems. [4] [7] [15] The MATLAB optimal control toolbox has been used to solve problems in aerospace, [11] robotics [1] and search theory. [2]
Octave (aka GNU Octave) is an alternative to MATLAB. Originally conceived in 1988 by John W. Eaton as a companion software for an undergraduate textbook, Eaton later opted to modify it into a more flexible tool. Development begun in 1992 and the alpha version was released in 1993. Subsequently, version 1.0 was released a year after that in 1994.
GPOPS-II [3] is designed to solve multiple-phase optimal control problems of the following mathematical form (where is the number of phases): = ((), …, ()) subject to the dynamic constraints
ScaLAPACK is a library of high-performance linear algebra routines for parallel distributed-memory machines that features functionality similar to LAPACK (solvers for dense and banded linear systems, least-squares problems, eigenvalue problems, and singular-value problem). Scilab is advanced numerical analysis package similar to MATLAB or Octave.
However, most problems in electronic design automation (EDA) are open problems, also called exterior problems, and since the fields decrease slowly towards infinity, these methods can require extremely large N. The second class of methods are integral equation methods which instead require a discretization of only electromagnetic field sources ...
The Robotics Toolbox for Python is a reimplementation of the Robotics Toolbox for MATLAB for Python 3. [ 7 ] [ 8 ] Its functionality is a superset of the Robotics Toolbox for MATLAB, the programming model is similar, and it supports additional methods to define a serial link manipulator including URDF and elementary transform sequences.
In numerical analysis, the shooting method is a method for solving a boundary value problem by reducing it to an initial value problem.It involves finding solutions to the initial value problem for different initial conditions until one finds the solution that also satisfies the boundary conditions of the boundary value problem.
Solving the general non-convex case is an NP-hard problem. To see this, note that the two constraints x 1 ( x 1 − 1) ≤ 0 and x 1 ( x 1 − 1) ≥ 0 are equivalent to the constraint x 1 ( x 1 − 1) = 0, which is in turn equivalent to the constraint x 1 ∈ {0, 1}.