The following important parameters (with these default values) were added:

relaxing/SDP/freq = 1	set this to -1 and lp/solvefreq to 1 to solve LP relaxations with eigenvector cuts
propagating/sdpredcost/freq = 1	set this to -1 to disable reduced cost fixing for SDPs
propagating/sdpredcost/advanced/forbins = TRUE	should sdp reduced cost fixing be executed for binary variables?
propagating/sdpredcost/advanced/forintcons = TRUE	should sdp reduced cost fixing be executed for integer and continuous variables?
relaxing/SDP/sdpsolverepsilon = 0.0001	sets the bound for the duality gap in the SDP-Solver
relaxing/SDP/sdpsolverfeastol = 0.00001	feasibility tolerance for the SDP-Solver (should be less or equal to numerics/feastol)
relaxing/SDP/sdpinfo = FALSE	should output of the SDP-Solver be printed to the console?
relaxing/SDP/objlimit = FALSE	should an objective limit be given to the SDP-Solver?
relaxing/SDP/slatercheck = FALSE	should the Slater condition for the dual problem be checked ahead of solving each SDP?
branching/sdpobjective/coupledvars = FALSE	if all branching candidates have objective zero, should we use the sum of the absolute objectives of all continuous variables coupled with the candidate through constraints?
branching/sdpobjective/singlecoupledvars = FALSE	if all branching candidates have objective zero, should we use the sum of the absolute objectives of all continuous variables coupled with the candidate through constraints in which no other candidate appears?
branching/sdpinfobjective/coupledvars = FALSE	if all branching candidates have objective zero, should we use the sum of the absolute objectives of all continuous variables coupled with the candidate through constraints?
branching/sdpinfobjective/singlecoupledvars = FALSE	if all branching candidates have objective zero, should we use the sum of the absolute objectives of all continuous variables coupled with the candidate through constraints in which no other candidate appears?