C++ BlockMatrix::addDense方法代码示例

本文整理汇总了C++中BlockMatrix::addDense方法的典型用法代码示例。如果您正苦于以下问题：C++ BlockMatrix::addDense方法的具体用法？C++ BlockMatrix::addDense怎么用？C++ BlockMatrix::addDense使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类BlockMatrix的用法示例。

在下文中一共展示了BlockMatrix::addDense方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: evaluateSensitivities

returnValue BoundaryConstraint::evaluateSensitivities( const DMatrix &seed, BlockMatrix &hessian ){

    // EVALUATION OF THE SENSITIVITIES:
    // --------------------------------

    int run1, run2;

    const int nc = getNC();
    const int N  = grid.getNumPoints();

    ASSERT( (int) seed.getNumRows() == nc );

    double *bseed1 = new double[nc];
    double *bseed2 = new double[nc];
    double *R      = new double[nc];

    double *J1      = new double[fcn[0].getNumberOfVariables() +1];
    double *H1      = new double[fcn[0].getNumberOfVariables() +1];
    double *fseed1  = new double[fcn[0].getNumberOfVariables() +1];

    double *J2      = new double[fcn[1].getNumberOfVariables() +1];
    double *H2      = new double[fcn[1].getNumberOfVariables() +1];
    double *fseed2  = new double[fcn[1].getNumberOfVariables() +1];

    for( run1 = 0; run1 < nc; run1++ ){
        bseed1[run1] = seed(run1,0);
        bseed2[run1] = 0.0;
    }

    for( run1 = 0; run1 < fcn[0].getNumberOfVariables()+1; run1++ )
        fseed1[run1] = 0.0;

    for( run1 = 0; run1 < fcn[1].getNumberOfVariables()+1; run1++ )
        fseed2[run1] = 0.0;

    dBackward.init( 1, 5*N );

    DMatrix Dx ( nc, nx );
    DMatrix Dxa( nc, na );
    DMatrix Dp ( nc, np );
    DMatrix Du ( nc, nu );
    DMatrix Dw ( nc, nw );

    DMatrix Hx ( nx, nx );
    DMatrix Hxa( nx, na );
    DMatrix Hp ( nx, np );
    DMatrix Hu ( nx, nu );
    DMatrix Hw ( nx, nw );

    for( run2 = 0; run2 < nx; run2++ ){

        // FIRST ORDER DERIVATIVES:
        // ------------------------
        fseed1[y_index[0][run2]] = 1.0;
        fcn[0].AD_forward( 0, fseed1, R );
        for( run1 = 0; run1 < nc; run1++ )
            Dx( run1, run2 ) = R[run1];
        fseed1[y_index[0][run2]] = 0.0;

        // SECOND ORDER DERIVATIVES:
        // -------------------------
        for( run1 = 0; run1 <= fcn[0].getNumberOfVariables(); run1++ ){
            J1[run1] = 0.0;
            H1[run1] = 0.0;
        }

        fcn[0].AD_backward2( 0, bseed1, bseed2, J1, H1 );

        for( run1 = 0          ; run1 < nx            ; run1++ ) Hx ( run2, run1             ) = -H1[y_index[0][run1]];
        for( run1 = nx         ; run1 < nx+na         ; run1++ ) Hxa( run2, run1-nx          ) = -H1[y_index[0][run1]];
        for( run1 = nx+na      ; run1 < nx+na+np      ; run1++ ) Hp ( run2, run1-nx-na       ) = -H1[y_index[0][run1]];
        for( run1 = nx+na+np   ; run1 < nx+na+np+nu   ; run1++ ) Hu ( run2, run1-nx-na-np    ) = -H1[y_index[0][run1]];
        for( run1 = nx+na+np+nu; run1 < nx+na+np+nu+nw; run1++ ) Hw ( run2, run1-nx-na-np-nu ) = -H1[y_index[0][run1]];
    }

    if( nx > 0 ){

        dBackward.setDense( 0, 0, Dx );

        if( nx > 0 ) hessian.addDense( 0,   0, Hx  );
        if( na > 0 ) hessian.addDense( 0,   N, Hxa );
        if( np > 0 ) hessian.addDense( 0, 2*N, Hp  );
        if( nu > 0 ) hessian.addDense( 0, 3*N, Hu  );
        if( nw > 0 ) hessian.addDense( 0, 4*N, Hw  );
    }

    Hx.init ( nx, nx );
    Hxa.init( nx, na );
    Hp.init ( nx, np );
    Hu.init ( nx, nu );
    Hw.init ( nx, nw );


    for( run2 = 0; run2 < nx; run2++ ){

        // FIRST ORDER DERIVATIVES:
        // ------------------------
        fseed2[y_index[1][run2]] = 1.0;
        fcn[1].AD_forward( 0, fseed2, R );
        for( run1 = 0; run1 < nc; run1++ )
//.........这里部分代码省略.........

开发者ID:OspreyX，项目名称:acado，代码行数:101，代码来源:boundary_constraint.cpp

示例2: evaluateSensitivities

returnValue PointConstraint::evaluateSensitivities( const DMatrix &seed, BlockMatrix &hessian ){

    // EVALUATION OF THE SENSITIVITIES:
    // --------------------------------

    int run1, run2;

    if( fcn == 0 ) return ACADOERROR(RET_MEMBER_NOT_INITIALISED);

    const int nc = fcn[0].getDim();
    const int N  = grid.getNumPoints();

    ASSERT( (int) seed.getNumRows() == nc );

    double *bseed1 = new double[nc];
    double *bseed2 = new double[nc];
    double *R      = new double[nc];
    double *J      = new double[fcn[0].getNumberOfVariables() +1];
    double *H      = new double[fcn[0].getNumberOfVariables() +1];
    double *fseed  = new double[fcn[0].getNumberOfVariables() +1];

    for( run1 = 0; run1 < nc; run1++ ){
        bseed1[run1] = seed(run1,0);
        bseed2[run1] = 0.0;
    }

    for( run1 = 0; run1 < fcn[0].getNumberOfVariables()+1; run1++ )
        fseed[run1] = 0.0;

    dBackward.init( 1, 5*N );

    DMatrix Dx ( nc, nx );
    DMatrix Dxa( nc, na );
    DMatrix Dp ( nc, np );
    DMatrix Du ( nc, nu );
    DMatrix Dw ( nc, nw );

    DMatrix Hx ( nx, nx );
    DMatrix Hxa( nx, na );
    DMatrix Hp ( nx, np );
    DMatrix Hu ( nx, nu );
    DMatrix Hw ( nx, nw );

    for( run2 = 0; run2 < nx; run2++ ){

        // FIRST ORDER DERIVATIVES:
        // ------------------------
        fseed[y_index[0][run2]] = 1.0;
        fcn[0].AD_forward( 0, fseed, R );
        for( run1 = 0; run1 < nc; run1++ )
            Dx( run1, run2 ) = R[run1];
        fseed[y_index[0][run2]] = 0.0;

        // SECOND ORDER DERIVATIVES:
        // -------------------------
        for( run1 = 0; run1 <= fcn[0].getNumberOfVariables(); run1++ ){
            J[run1] = 0.0;
            H[run1] = 0.0;
        }

        fcn[0].AD_backward2( 0, bseed1, bseed2, J, H );

        for( run1 = 0          ; run1 < nx            ; run1++ ) Hx ( run2, run1             ) = -H[y_index[0][run1]];
        for( run1 = nx         ; run1 < nx+na         ; run1++ ) Hxa( run2, run1-nx          ) = -H[y_index[0][run1]];
        for( run1 = nx+na      ; run1 < nx+na+np      ; run1++ ) Hp ( run2, run1-nx-na       ) = -H[y_index[0][run1]];
        for( run1 = nx+na+np   ; run1 < nx+na+np+nu   ; run1++ ) Hu ( run2, run1-nx-na-np    ) = -H[y_index[0][run1]];
        for( run1 = nx+na+np+nu; run1 < nx+na+np+nu+nw; run1++ ) Hw ( run2, run1-nx-na-np-nu ) = -H[y_index[0][run1]];
    }

    if( nx > 0 ){

        dBackward.setDense( 0, point_index, Dx );

        if( nx > 0 ) hessian.addDense( point_index,       point_index, Hx  );
        if( na > 0 ) hessian.addDense( point_index,   N + point_index, Hxa );
        if( np > 0 ) hessian.addDense( point_index, 2*N + point_index, Hp  );
        if( nu > 0 ) hessian.addDense( point_index, 3*N + point_index, Hu  );
        if( nw > 0 ) hessian.addDense( point_index, 4*N + point_index, Hw  );
    }

    Hx.init ( na, nx );
    Hxa.init( na, na );
    Hp.init ( na, np );
    Hu.init ( na, nu );
    Hw.init ( na, nw );

    for( run2 = nx; run2 < nx+na; run2++ ){

        // FIRST ORDER DERIVATIVES:
        // ------------------------
        fseed[y_index[0][run2]] = 1.0;
        fcn[0].AD_forward( 0, fseed, R );
        for( run1 = 0; run1 < nc; run1++ )
            Dxa( run1, run2-nx ) = R[run1];
        fseed[y_index[0][run2]] = 0.0;

        // SECOND ORDER DERIVATIVES:
        // -------------------------
        for( run1 = 0; run1 <= fcn[0].getNumberOfVariables(); run1++ ){
            J[run1] = 0.0;
//.........这里部分代码省略.........

开发者ID:OspreyX，项目名称:acado，代码行数:101，代码来源:point_constraint.cpp

示例3: evaluateSensitivities

returnValue ShootingMethod::evaluateSensitivities( const BlockMatrix &seed, BlockMatrix &hessian ){

    const int NN = N+1;
    dForward.init( N, 5 );
    int i;

    for( i = 0; i < N; i++ ){

        Matrix X, P, U, W, D, E, HX, HP, HU, HW, S;

        if( xSeed.isEmpty() == BT_FALSE ) xSeed.getSubBlock( i, 0, X );
        if( pSeed.isEmpty() == BT_FALSE ) pSeed.getSubBlock( i, 0, P );
        if( uSeed.isEmpty() == BT_FALSE ) uSeed.getSubBlock( i, 0, U );
        if( wSeed.isEmpty() == BT_FALSE ) wSeed.getSubBlock( i, 0, W );

        seed.getSubBlock( i, 0, S, nx, 1 );

        if( nx > 0 ){

            ACADO_TRY( differentiateForwardBackward( i, X, E, E, E, S, D, HX, HP, HU, HW ));
            dForward.setDense( i, 0, D );

            if( nx > 0 ) hessian.addDense( i,      i, HX );
            if( np > 0 ) hessian.addDense( i, 2*NN+i, HP );
            if( nu > 0 ) hessian.addDense( i, 3*NN+i, HU );
            if( nw > 0 ) hessian.addDense( i, 4*NN+i, HW );
        }

        if( np > 0 ){

            ACADO_TRY( differentiateForwardBackward( i, E, P, E, E, S, D, HX, HP, HU, HW ));
            dForward.setDense( i, 2, D );

            if( nx > 0 ) hessian.addDense( 2*NN+i,      i, HX );
            if( np > 0 ) hessian.addDense( 2*NN+i, 2*NN+i, HP );
            if( nu > 0 ) hessian.addDense( 2*NN+i, 3*NN+i, HU );
            if( nw > 0 ) hessian.addDense( 2*NN+i, 4*NN+i, HW );
        }

        if( nu > 0 ){

            ACADO_TRY( differentiateForwardBackward( i, E, E, U, E, S, D, HX, HP, HU, HW ));
            dForward.setDense( i, 3, D );

            if( nx > 0 ) hessian.addDense( 3*NN+i,      i, HX );
            if( np > 0 ) hessian.addDense( 3*NN+i, 2*NN+i, HP );
            if( nu > 0 ) hessian.addDense( 3*NN+i, 3*NN+i, HU );
            if( nw > 0 ) hessian.addDense( 3*NN+i, 4*NN+i, HW );
        }

        if( nw > 0 ){

            ACADO_TRY( differentiateForwardBackward( i, E, E, E, W, S, D, HX, HP, HU, HW ));
            dForward.setDense( i, 4, D );

            if( nx > 0 ) hessian.addDense( 4*NN+i,      i, HX );
            if( np > 0 ) hessian.addDense( 4*NN+i, 2*NN+i, HP );
            if( nu > 0 ) hessian.addDense( 4*NN+i, 3*NN+i, HU );
            if( nw > 0 ) hessian.addDense( 4*NN+i, 4*NN+i, HW );
        }
    }
    return SUCCESSFUL_RETURN;
}

开发者ID:rtkg，项目名称:acado，代码行数:63，代码来源:shooting_method.cpp

注：本文中的BlockMatrix::addDense方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。