This manual page documents briefly the
csdp -- interface to solve general semi-definite programs
csdp-complement -- compute the complement of a graph and output it in csdp problem format
csdp-graphtoprob -- convert graph into csdp problem format file
csdp-randgraph -- generate a random graph
csdp-theta -- solves the Lovasz thetha problem
A summary of options is included below.
For a complete description, see
in the SDPA sparse format
is the name of a file containing the SDP problem in SDPA sparse format
is the optional name of a file in which to save the final solution
is the optional name of a file from which to take the initial solution.
CSDP searches for a file named
in the current directory. If
no such file exists, then default values for all of CSDP’s parameters are used. If
there is a parameter file, then CSDP reads the parameter values from this file.
The default parameter values is given below (can be pasted into a file):
tolerances for primal feasibility, dual feasibility, and relative duality gap
tolerances used in determining primal and dual infeasibility
plimit the total number of iterations that CSDP may use
determine how close to the edge of the feasible region CSDP will step. If the primal or dual step is
shorter than minstepp or minstepd, then CSDP declares a line search failure.
If parameter 0, then CSDP will use the objective function duality
gap instead of the tr(XZ) gap
if set to 1, and usexzgap is set to 0, then CSDP will attempt to "fix" negative duality gaps.
If parameter affine
is set to 1, then CSDP will take only primal–dual affine steps and not make
use of the barrier term. This can be useful for some problems that do not have
feasible solutions that are strictly in the interior of the cone of semidefinite ma-
determines how much debugging information is output. Use printlevel=0 for no
output and printlevel=1 for normal output.
Higher values of printlevel will generate more debugging output.
determines whether the objective function will be perturbed
to help deal with problems that have unbounded optimal solution sets. If per-
turbobj is 0, then the objective will not be perturbed. If perturbobj=1, then
the objective function will be perturbed by a default amount. Larger values
of perturbobj (e.g. 100.0) increase the size of the perturbation. This can be
helpful in solving some difficult problems.
determines whether or not CSDP will skip certain time consuming operations that slightly
improve the accuracy of the solutions. If fastmode is set to 1, then CSDP may
be somewhat faster, but also somewhat less accurate.