! \fn int DSDPSetMaximumLanczosIterations( DSDPLanczosStepLength *LZ, int maxlanczos ); \brief Set parameter. \param LZ structure. \param maxlanczos a

! \fn int DSDPSetRR(DSDP dsdp,double res); \param dsdp solver \param res variable r \brief Set variable r. */

! \file printsdpa.c \brief Print data or solution in SDPA format */

* IDADlsSetBandJacFn specifies the band Jacobian function. */

* IDADlsSetDenseJacFn specifies the dense Jacobian function. */

* IDASlsSetSparseJacFn specifies the sparse Jacobian function. */

Creates a new (empty) sparse matrix of a desired size and nonzero density. Returns NULL if a memory allocation error occurred. */

! \fn int SDPConeVecNormalize(SDPConeVec V) \brief Scale the vector to norm of 1. \param V the vector */

! \fn int SDPConeVecScale(double alpha, SDPConeVec VV) \brief Compute the Euclidean norm. \param alpha scalar. \param VV a vec */

! \fn int SDPConeVecSet(double alpha, SDPConeVec V) \brief Set each element of vector to this number. \param alpha scalar. \param V a vec */

@{ \brief Arc sine: x -> asin(x) \identifier{p7} */

Alias names

p [0..n] = cumulative sum of c [0..n-1], and then copy p [0..n-1] into c */

drop entries for which fkeep(A(i,j)) is false; return nz if OK, else -1 */

solve L'x=b where x and b are dense. x=b on input, solution on output. */

x = x + beta * A(:,j), where x is a dense vector and A(:,j) is sparse */

symbolic ordering and analysis for QR or LU */

Creates and returns a pydot graph structure that represents an SX. direction one of "BT", "LR", "TB", "RL"

* f l i p F i x e d */

@{ \brief Smallest of two values: (x,y) -> min(x,y) \identifier{pt} */

* g e t */

* g e t D u a l S o l u t i o n */

* g e t I S o r t A r r a y */

* g e t P r i m a l S o l u t i o n */

\brief Print free variables \identifier{uo} */

Create a horizontal concatenation node