- Version 0.5: in development phase
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A first version of SQPlab for solving optimal control problems with only equality constraints has been implemented.
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Changes in the options
- algo_method has been deleted and more or less replaced by algo_hessian,
- algo_hessian specifies whether the Hessian of the Lagrangian has to be computed by the simulator (value
'Newton') or whether a quasi-Newton approximation must be computed by SQPlab (value 'quasi-Newton').
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Changes in the directory examples (previously called example)
- hanging_chain is now named hanging_chain_02,
- two more hanging chain examples have been added with the goal to illustrate how to write the simulator for an optimal
control problem with only equality constraints: in hanging_chain_01 the problem is solved by the standard SQP
algorithm and in hanging_chain_01b the same problem is viewed as an optimal control problem and is solved by the
reduced SQP algorithm.
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Cosmetic improvements.
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All these modifications lack of intensive testing (only trials on the three hanging chain problems have been realized).
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Version 0.4.5: December 2011
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The hanging chain example in the example directory has been corrected.
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Version 0.4.4: February 2009
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Bug corrected: the unconstrained quasi-Newton version with Wolfe linesearch was not terminated ... (Thanks to Xiaohan Ma, for
having mentioned that).
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Cosmetic improvements.
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Version 0.4.3: December 2008
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The Hanging Chain example has been added.
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Version 0.4.2: December 2008
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Bugs corrected.
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Cosmetic improvements.
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Version 0.4.1: October 2007
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Cosmetic changes.
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Version 0.4: June 2007
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Unconstrained problems can now be solved by a quasi-Newton algorithm
using Wolfe linesearch and the inverse BFGS update formula.
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Important restructuration of the software.
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Version 0.3: March 2007
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State constraints are accepted, provided there is no inequality
constraints and the Newton version of the algorithm is used.
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Version 0.2: February 2007
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Globalization by linesearch, using a merit function.
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Quasi-Newton (BFGS) approximation of the Hessian of the Lagrangian.
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Version 0.1: December 2006
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The local algorithms (equality and inequality constraints) using second
derivatives.