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custom source code transformation tool  (OpenSim Ltd)

 
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    OpenSim Ltd custom source code transformation tool
    We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function F and its forward ( F fwd ) and reverse ( F rev ) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ <t>code</t> to provide F fwd and F rev . In the AD-Recorder approach (bottom), Recorder provides the expression graph of F as MATLAB <t>source</t> code from which CasADi’s C-code generator generates C-code containing F , F fwd , and F rev . The AD-Recorder approach combines operator overloading, when generating the expression graph, and source code <t>transformation,</t> when processing the expression graph to generate C-code for F , F fwd , and F rev . In both approaches, the code comprising F , F fwd , and F rev is compiled as a Dynamic-link Library (DLL), which is imported as an external function within the CasADi environment. In our application, F represents the multi-body dynamics and is called when formulating the optimal control problem. The latter is then composed into a differentiable optimal control transcription using CasADi. During the optimization, CasADi provides the NLP solver with evaluations of the NLP objective function ( nlp f ), constraints ( nlp g ), objective function gradient ( nlp grad f ), constraint Jacobian ( nlp jac g ), and Hessian of the Lagrangian ( nlp hess l ). CasADi efficiently queries F fwd and F rev to construct the full derivative matrices.
    Custom Source Code Transformation Tool, supplied by OpenSim Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/-coded+custom+tool/custom+source+code+transformation+tool/pmc06797126-28-11-29
    Average 90 stars, based on 1 article reviews
    custom source code transformation tool - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement"

    Article Title: Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement

    Journal: PLoS ONE

    doi: 10.1371/journal.pone.0217730

    We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function F and its forward ( F fwd ) and reverse ( F rev ) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ code to provide F fwd and F rev . In the AD-Recorder approach (bottom), Recorder provides the expression graph of F as MATLAB source code from which CasADi’s C-code generator generates C-code containing F , F fwd , and F rev . The AD-Recorder approach combines operator overloading, when generating the expression graph, and source code transformation, when processing the expression graph to generate C-code for F , F fwd , and F rev . In both approaches, the code comprising F , F fwd , and F rev is compiled as a Dynamic-link Library (DLL), which is imported as an external function within the CasADi environment. In our application, F represents the multi-body dynamics and is called when formulating the optimal control problem. The latter is then composed into a differentiable optimal control transcription using CasADi. During the optimization, CasADi provides the NLP solver with evaluations of the NLP objective function ( nlp f ), constraints ( nlp g ), objective function gradient ( nlp grad f ), constraint Jacobian ( nlp jac g ), and Hessian of the Lagrangian ( nlp hess l ). CasADi efficiently queries F fwd and F rev to construct the full derivative matrices.
    Figure Legend Snippet: We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function F and its forward ( F fwd ) and reverse ( F rev ) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ code to provide F fwd and F rev . In the AD-Recorder approach (bottom), Recorder provides the expression graph of F as MATLAB source code from which CasADi’s C-code generator generates C-code containing F , F fwd , and F rev . The AD-Recorder approach combines operator overloading, when generating the expression graph, and source code transformation, when processing the expression graph to generate C-code for F , F fwd , and F rev . In both approaches, the code comprising F , F fwd , and F rev is compiled as a Dynamic-link Library (DLL), which is imported as an external function within the CasADi environment. In our application, F represents the multi-body dynamics and is called when formulating the optimal control problem. The latter is then composed into a differentiable optimal control transcription using CasADi. During the optimization, CasADi provides the NLP solver with evaluations of the NLP objective function ( nlp f ), constraints ( nlp g ), objective function gradient ( nlp grad f ), constraint Jacobian ( nlp jac g ), and Hessian of the Lagrangian ( nlp hess l ). CasADi efficiently queries F fwd and F rev to construct the full derivative matrices.

    Techniques Used: Expressing, Transformation Assay, Control, Construct

    Related Articles

    Transformation Assay:

    Article Title: Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement
    Article Snippet: .. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to solve trajectory optimization problems. ..

    other:

    Article Title: An open-source model and solution method to predict co-contraction in the finger.
    Article Snippet: A novel open-source biomechanical model of the index finger with an electromyography (EMG)constrained static optimization solution method are developed with the goal of improving cocontraction estimates and providing means to assess tendon tension distribution through the finger.. The Intrinsic model has four degrees of freedom and seven muscles (with a 14 component extensor mechanism).. A novel plugin developed for the OpenSim modelling software applied the EMG-constrained static optimization solution method.



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    We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function F and its forward ( F fwd ) and reverse ( F rev ) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ <t>code</t> to provide F fwd and F rev . In the AD-Recorder approach (bottom), Recorder provides the expression graph of F as MATLAB <t>source</t> code from which CasADi’s C-code generator generates C-code containing F , F fwd , and F rev . The AD-Recorder approach combines operator overloading, when generating the expression graph, and source code <t>transformation,</t> when processing the expression graph to generate C-code for F , F fwd , and F rev . In both approaches, the code comprising F , F fwd , and F rev is compiled as a Dynamic-link Library (DLL), which is imported as an external function within the CasADi environment. In our application, F represents the multi-body dynamics and is called when formulating the optimal control problem. The latter is then composed into a differentiable optimal control transcription using CasADi. During the optimization, CasADi provides the NLP solver with evaluations of the NLP objective function ( nlp f ), constraints ( nlp g ), objective function gradient ( nlp grad f ), constraint Jacobian ( nlp jac g ), and Hessian of the Lagrangian ( nlp hess l ). CasADi efficiently queries F fwd and F rev to construct the full derivative matrices.
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    Image Search Results


    We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function F and its forward ( F fwd ) and reverse ( F rev ) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ code to provide F fwd and F rev . In the AD-Recorder approach (bottom), Recorder provides the expression graph of F as MATLAB source code from which CasADi’s C-code generator generates C-code containing F , F fwd , and F rev . The AD-Recorder approach combines operator overloading, when generating the expression graph, and source code transformation, when processing the expression graph to generate C-code for F , F fwd , and F rev . In both approaches, the code comprising F , F fwd , and F rev is compiled as a Dynamic-link Library (DLL), which is imported as an external function within the CasADi environment. In our application, F represents the multi-body dynamics and is called when formulating the optimal control problem. The latter is then composed into a differentiable optimal control transcription using CasADi. During the optimization, CasADi provides the NLP solver with evaluations of the NLP objective function ( nlp f ), constraints ( nlp g ), objective function gradient ( nlp grad f ), constraint Jacobian ( nlp jac g ), and Hessian of the Lagrangian ( nlp hess l ). CasADi efficiently queries F fwd and F rev to construct the full derivative matrices.

    Journal: PLoS ONE

    Article Title: Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement

    doi: 10.1371/journal.pone.0217730

    Figure Lengend Snippet: We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function F and its forward ( F fwd ) and reverse ( F rev ) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. In the AD-ADOLC approach (top), ADOL-C’s algorithms are used in a C++ code to provide F fwd and F rev . In the AD-Recorder approach (bottom), Recorder provides the expression graph of F as MATLAB source code from which CasADi’s C-code generator generates C-code containing F , F fwd , and F rev . The AD-Recorder approach combines operator overloading, when generating the expression graph, and source code transformation, when processing the expression graph to generate C-code for F , F fwd , and F rev . In both approaches, the code comprising F , F fwd , and F rev is compiled as a Dynamic-link Library (DLL), which is imported as an external function within the CasADi environment. In our application, F represents the multi-body dynamics and is called when formulating the optimal control problem. The latter is then composed into a differentiable optimal control transcription using CasADi. During the optimization, CasADi provides the NLP solver with evaluations of the NLP objective function ( nlp f ), constraints ( nlp g ), objective function gradient ( nlp grad f ), constraint Jacobian ( nlp jac g ), and Hessian of the Lagrangian ( nlp hess l ). CasADi efficiently queries F fwd and F rev to construct the full derivative matrices.

    Article Snippet: First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to solve trajectory optimization problems.

    Techniques: Expressing, Transformation Assay, Control, Construct