options = sdpsettings('field',value,'field',value,...) optimize(Constraints, Objective, options)
Creating an options structure with specified values is easily done.
ops = sdpsettings('solver','mosek','verbose',1,'debug',1)
A convenient way to alter many options without getting a long line is to send an existing options structure as the first input argument.
ops = sdpsettings('solver','sdpa'); ops = sdpsettings(ops,'verbose',0);
Alternatively, the options structure can be manipulated directly
ops = sdpsettings('solver','sdpa'); ops.verbose = 0;
ops = sdpsettings('solver','sdpa','sdpa.maxIteration',100);
The easiest way to find out the possible options is to define a default options structure and display it
ops = sdpsettings; ops solver: '' verbose: 1 warning: 1 cachesolvers: 0 debug: 1 beeponproblem: [-5 -4 -3 -2 -1] showprogress: 0 saveduals: 1 removeequalities: 0 savesolveroutput: 0 savesolverinput: 0 convertconvexquad: 1 radius: Inf relax: 0 usex0: 0 savedebug: 0 sos: [1x1 struct] moment: [1x1 struct] bnb: [1x1 struct] bpmpd: [1x1 struct] bmibnb: [1x1 struct] cutsdp: [1x1 struct] global: [1x1 struct] cdd: [1x1 struct] clp: [1x1 struct] cplex: [1x1 struct] csdp: [1x1 struct] dsdp: [1x1 struct] glpk: [1x1 struct] kypd: [1x1 struct] lmilab: [1x1 struct] lmirank: [1x1 struct] lpsolve: [1x1 struct] maxdet: [1x1 struct] nag: [1x1 struct] penbmi: [1x1 struct] pennlp: [1x1 struct] pensdp: [1x1 struct] sdpa: [1x1 struct] sdplr: [1x1 struct] sdpt3: [1x1 struct] sedumi: [1x1 struct] qsopt: [1x1 struct] xpress: [1x1 struct] quadprog: [1x1 struct] linprog: [1x1 struct] bintprog: [1x1 struct] fmincon: [1x1 struct] fminsearch: [1x1 struct] ops.sedumi ans = alg: 2 beta: 0.5000 theta: 0.2500 free: 1 sdp: 0 stepdif: 0 w: [1 1] mu: 1 eps: 1.0000e-09 bigeps: 1.0000e-03 maxiter: 150 vplot: 0 stopat: -1 denq: 0.7500 denf: 10 numtol: 5.0000e-07 bignumtol: 0.9000 numlvlv: 0 chol: [1x1 struct] cg: [1x1 struct] maxradius: Inf
In the code above, we told YALMIP to use the solver Mosek. The possible values to give to the field solver can be found in the solver documentation. If the solver isn’t found, an error code will be returned in the output structure. To let YALMIP select the solver, use the default solver tag ‘’. If you give a comma-separated list of solvers such as ‘dsdp,csdp,sdpa’, YALMIP will select based on this preference. If you add a wildcard in the end ‘dsdp,csdp,sdpa,*‘, YALMIP will select another solver if none of the solvers in the list were found.
By setting verbose to 0, the solvers will run with minimal display. By increasing the value, the display level is controlled (typically 1 gives modest display level while 2 gives an awful amount of information printed).
If debug is turned on, YALMIP will not try to catch errors, which will simplify finding out where and why YALMIP failed unexpectedly.
The warning option can be used to get a warning displayed when a solver has run into some kind of problem (recommended to be used if the solver is run in silent mode).
The field beeponproblem contains a list of error codes (see yalmiperror). YALMIP will beep if any of these errors occurs (nice feature if you’re taking a coffee break during heavy calculations).
When the field showprogress is set to 1, the user can see what YALMIP currently is doing (might be a good idea for large-scale problems).
Every time optimize is called, YALMIP checks for available solvers. This can take a while on some systems (some slow networks), so it is possible to avoid doing this check every call. Set cachesolvers to 1, and YALMIP will remember the solvers (using a persistent variable) found in the first call to optimize. If solvers are added to the path after the first call, YALMIP will not detect this. Hence, after adding a solver to the path the work-space must be cleared or optimize must be called once with cachesolvers set to 0. Only use this option if you absolutely have to, this is practically obsolete on modern systems.
When the field removeequalities is set to 1, YALMIP removes equality constraints using a QR decomposition and reformulates the problem using a smaller set of variables.
If removeequalities is set to 2, YALMIP removes equality constraints using a basis derived directly from independent columns of the equality constraints (higher possibility of maintaining sparsity than the QR approach, but may lead to a numerically poor basis).
With removeequalities set to -1, equalities are removed by YALMIP by converting them to double-sided inequalities. When set to 0 (default), YALMIP does nothing if the solver supports equalities. If the solver does not support equalities, YALMIP uses double-sided inequalities.
If saveduals is set to 0, the dual variables will not be saved in YALMIP. This might be useful for large sparse problems with a dense dual variable. Setting the field to 0 will then save some memory.
The fields savesolverinput and savesolveroutput can be used to see what is actually sent to and returned from the solver. This data will then be available in the output structure from optimize.
With convertconvexquad set to 1 (default), YALMIP will try to convert quadratic constraints to second order cones.
If relax is set to 1, all nonlinearities and integrality constraints will be disregarded. Integer variables are relaxed to continuous variables and nonlinear variables are treated as independent variables (i.e., x and x^2 will be treated as two separate variables). If set to 2, only integrality constraints are relaxed, while set to 3 only nonlinearities are relaxed.
The current solution (the value returned from the value command) can be used as an initial guess when solving an optimization problem. Setting the field usex0 to 1 tells YALMIP to supply the current values as an initial guess to the solver. You can manually specify a current value using assign.
The options structure also contains a number of structures with parameters used in the specific solver. As an example, the following parameters can be tuned for SEDUMI (for details, the user is referred to the manual of the specific solver).
ops.sedumi ans = alg: 2 theta: 0.2500 beta: 0.5000 eps: 1.0000e-009 bigeps: 0.0010 numtol: 1.0000e-005 denq: 0.7500 denf: 10 vplot: 0 maxiter: 100 stepdif: 1 w: [1 1] stopat: -1 cg: [1x1 struct] chol: [1x1 struct]
Hence, the following code will select SeDuMi and change the default precision
ops = sdpsettings('solver','sedumi','sedumi.eps',1e-12);