Problem¶

class
picos.
Problem
(**options)¶ This class represents an optimization problem. The constructor creates an empty problem. Some options can be provided under the form
key = value
. See the list of available options in the doc ofset_all_options_to_default()

add_constraint
(cons, key=None, ret=False)¶ Adds a constraint in the problem.
Parameters:  cons (
Constraint
) – The constraint to be added.  key (str.) – Optional parameter to describe the constraint with a key string.
 ret (bool.) – Do you want the added constraint to be returned ?
This can be a useful handle to extract the optimal dual variable of this constraint
or to delete the constraint with delete().
Note: The constraint is always returned if the option
return_constraints
is set toTrue
.
 cons (

add_list_of_constraints
(lst, it=None, indices=None, key=None, ret=False)¶ adds a list of constraints in the problem. This fonction can be used with python list comprehensions (see the example below).
Parameters:  lst – list of
Constraint
.  it (None or str or list.) – Description of the letters which should
be used to replace the dummy indices.
The function tries to find a template
for the string representations of the
constraints in the list. If several indices change in the
list, their letters should be given as a
list of strings, in their order of appearance in the
resulting string. For example, if three indices
change in the constraints, and you want them to be named
'i'
,'j'
and'k'
, setit = ['i','j','k']
. You can also group two indices which always appear together, e.g. if'i'
always appear next to'j'
you could setit = [('ij',2),'k']
. Here, the number 2 indicates that'ij'
replaces 2 indices. Ifit
is set toNone
, or if the function is not able to find a template, the string of the first constraint will be used for the string representation of the list of constraints.  indices (str.) – a string to denote the set where the indices belong to.
 key (str.) – Optional parameter to describe the list of constraints with a key string.
 ret (bool.) – Do you want the added list of constraints to be returned ?
This can be useful to access the duals of these constraints.
Note: The constraint is always returned if the option
return_constraints
is set toTrue
.
Example:
>>> import picos as pic >>> import cvxopt as cvx >>> prob=pic.Problem() >>> x=[prob.add_variable('x[{0}]'.format(i),2) for i in range(5)] >>> x [# variable x[0]:(2 x 1),continuous #, # variable x[1]:(2 x 1),continuous #, # variable x[2]:(2 x 1),continuous #, # variable x[3]:(2 x 1),continuous #, # variable x[4]:(2 x 1),continuous #] >>> y=prob.add_variable('y',5) >>> IJ=[(1,2),(2,0),(4,2)] >>> w={} >>> for ij in IJ: ... w[ij]=prob.add_variable('w[{0}]'.format(ij),3) ... >>> u=pic.new_param('u',cvx.matrix([2,5])) >>> prob.add_list_of_constraints( ... [u.T*x[i]<y[i] for i in range(5)], ... 'i', ... '[5]') >>> >>> prob.add_list_of_constraints( ... [abs(w[i,j])<y[j] for (i,j) in IJ], ... [('ij',2)], ... 'IJ') >>> >>> prob.add_list_of_constraints( ... [y[t] > y[t+1] for t in range(4)], ... 't', ... '[4]') >>> >>> print prob  optimization problem (SOCP): 24 variables, 9 affine constraints, 12 vars in 3 SO cones x : list of 5 variables, (2, 1), continuous w : dict of 3 variables, (3, 1), continuous y : (5, 1), continuous find vars such that u.T*x[i] < y[i] for all i in [5] w[ij] < y[ij__1] for all ij in IJ y[t] > y[t+1] for all t in [4] 
 lst – list of

add_variable
(name, size=1, vtype='continuous', lower=None, upper=None)¶ adds a variable in the problem, and returns the corresponding instance of the
Variable
.For example,
>>> prob=pic.Problem() >>> x=prob.add_variable('x',3) >>> x # variable x:(3 x 1),continuous #
Parameters:  name (str.) – The name of the variable.
 size (int or tuple.) –
The size of the variable.
Can be either:
 an
int
n , in which case the variable is a vector of dimension n  or a
tuple
(n,m), and the variable is a n x mmatrix.
 an
 vtype (str.) –
variable
type
. Can be:'continuous'
(default),'binary'
: 0/1 variable'integer'
: integer valued variable'symmetric'
: symmetric matrix'antisym'
: antisymmetric matrix'complex'
: complex matrix variable'hermitian'
: complex hermitian matrix'semicont'
: 0 or continuous variable satisfying its bounds (supported by CPLEX and GUROBI only)'semiint'
: 0 or integer variable satisfying its bounds (supported by CPLEX and GUROBI only)
 lower (Any type recognized by the function
_retrieve_matrix()
.) – a lower bound for the variable. Can be either a vector/matrix of the same size as the variable, or a scalar (in which case all elements of the variable have the same lower bound).  upper (Any type recognized by the function
_retrieve_matrix()
.) – an upper bound for the variable. Can be either a vector/matrix of the same size as the variable, or a scalar (in which case all elements of the variable have the same upper bound).
Returns: An instance of the class
Variable
.

check_current_value_feasibility
(tol=1e05)¶ returns
True
if the current value of the variabless is a feasible solution, up to the tolerancetol
. Iftol
is set toNone
, the option parameteroptions['tol']
is used instead. The integer feasibility is checked with a tolerance of 1e3.

constraints
= None¶ list of all constraints

convert_quad_to_socp
()¶ replace quadratic constraints by equivalent second order cone constraints

convert_quadobj_to_constraint
()¶ replace quadratic objective by equivalent quadratic constraint

copy
()¶ creates a copy of the problem.

countCons
= None¶ numner of (multidimensional) constraints

countGeomean
= None¶ number of geomean (and other nonstandard convex) inequalities

countVar
= None¶ number of (multidimensional) variables

dualize
()¶ Returns a Problem containing the Lagrangian dual of the current problem
self
. More precisely, the current problem is parsed as a problem in a canonical primal form (cf. the note on dual variables of the tutorial), and the corresponding dual form is returned.

get_constraint
(ind)¶ returns a constraint of the problem.
Parameters: ind (int or tuple.) – There are two ways to index a constraint.
 if
ind
is an int , then the nth constraint (starting from 0) will be returned, where all the constraints are counted in the order where they were passed to the problem.  if
ind
is a tuple , then the ith constraint from the kth group of constraints is returned (starting from 0). By group of constraints, it is meant a single constraint or a list of constraints added together with the functionadd_list_of_constraints()
.  if
ind
is a tuple of length 1 , then the list of constraints of the kth group is returned.
Example:
>>> import picos as pic >>> import cvxopt as cvx >>> prob=pic.Problem() >>> x=[prob.add_variable('x[{0}]'.format(i),2) for i in range(5)] >>> y=prob.add_variable('y',5) >>> prob.add_list_of_constraints( ... [(1x[i])<y[i] for i in range(5)], ... 'i', ... '[5]') >>> prob.add_constraint(y>0) >>> print prob  optimization problem (LP): 15 variables, 10 affine constraints x : list of 5 variables, (2, 1), continuous y : (5, 1), continuous find vars such that 〈 1  x[i] 〉 < y[i] for all i in [5] y > 0  >>> prob.get_constraint(1) #2d constraint (numbered from 0) # (1x1)affine constraint: 〈 1  x[1] 〉 < y[1] # >>> prob.get_constraint((0,3)) #4th consraint from the 1st group # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] # >>> prob.get_constraint((1,)) #unique constraint of the 2d 'group' # (5x1)affine constraint: y > 0 # >>> prob.get_constraint((0,)) #list of constraints of the 1st group [# (1x1)affine constraint: 〈 1  x[0] 〉 < y[0] #, # (1x1)affine constraint: 〈 1  x[1] 〉 < y[1] #, # (1x1)affine constraint: 〈 1  x[2] 〉 < y[2] #, # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] #, # (1x1)affine constraint: 〈 1  x[4] 〉 < y[4] #] >>> prob.get_constraint(5) #6th constraint # (5x1)affine constraint: y > 0 #
 if

get_valued_variable
(name)¶ Returns the value of the variable (as an
cvxopt matrix
) with the givenname
. Ifname
refers to a list (resp. dict) of variables, named with the templatename[index]
(resp.name[key]
), then the function returns the list (resp. dict) of these variables.Parameters: name (str.) – name of the variable, or of a list/dict of variables. Warning
If the problem has not been solved, or if the variable is not valued, this function will raise an Exception.

get_variable
(name)¶ Returns the variable (as a
Variable
) with the givenname
. Ifname
refers to a list (resp. dict) of variables, named with the templatename[index]
(resp.name[key]
), then the function returns the list (resp. dict) of these variables.Parameters: name (str.) – name of the variable, or of a list/dict of variables.

is_continuous
()¶ Returns
True
if there are only continuous variables

numberAffConstraints
= None¶ total number of (scalar) affine constraints

numberConeConstraints
= None¶ number of SOC constraints

numberConeVars
= None¶ number of auxilary variables required to handle the SOC constraints

numberLSEConstraints
= None¶ number of LogSumExp constraints (+1 if the objective is a LogSumExp)

numberLSEVars
= None¶ number of vars in LogSumExp expressions

numberOfVars
= None¶ total number of (scalar) variables

numberQuadConstraints
= None¶ number of quadratic constraints (+1 if the objective is quadratic)

numberQuadNNZ
= None¶ number of nonzero entries in the matrices defining the quadratic expressions

numberSDPConstraints
= None¶ number of SDP constraints

numberSDPVars
= None¶ size of the svecotrized matrices involved in SDP constraints

obj_passed
= None¶ list of solver instances where the objective has been passed

obj_value
()¶ If the problem was already solved, returns the objective value. Otherwise, it raises an
AttributeError
.

remove_all_constraints
()¶ Removes all constraints from the problem This function does not remove hardcoded bounds on variables; use the function
remove_all_variable_bounds()
to do so.

remove_all_variable_bounds
()¶ remove all the lower and upper bounds on variables (i.e,, hardcoded bounds passed in the attribute
bnd
of the variables.

remove_constraint
(ind)¶ Deletes a constraint or a list of constraints of the problem.
Parameters: ind (int or tuple.) – The indexing of constraints works as in the function
get_constraint()
: if
ind
is an integer , the nth constraint (numbered from 0) is deleted  if
ind
is a tuple , then the ith constraint from the kth group of constraints is deleted (starting from 0). By group of constraints, it is meant a single constraint or a list of constraints added together with the functionadd_list_of_constraints()
.  if
ind
is a tuple of length 1 , then the whole kth group of constraints is deleted.
Example:
>>> import picos as pic >>> import cvxopt as cvx >>> prob=pic.Problem() >>> x=[prob.add_variable('x[{0}]'.format(i),2) for i in range(4)] >>> y=prob.add_variable('y',4) >>> prob.add_list_of_constraints( ... [(1x[i])<y[i] for i in range(4)], 'i', '[5]') >>> prob.add_constraint(y>0) >>> prob.add_list_of_constraints( ... [x[i]<2 for i in range(3)], 'i', '[3]') >>> prob.add_constraint(x[3]<1) >>> prob.constraints [# (1x1)affine constraint: 〈 1  x[0] 〉 < y[0] #, # (1x1)affine constraint: 〈 1  x[1] 〉 < y[1] #, # (1x1)affine constraint: 〈 1  x[2] 〉 < y[2] #, # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] #, # (4x1)affine constraint: y > 0 #, # (2x1)affine constraint: x[0] < 2.0 #, # (2x1)affine constraint: x[1] < 2.0 #, # (2x1)affine constraint: x[2] < 2.0 #, # (2x1)affine constraint: x[3] < 1 #] >>> prob.remove_constraint(1) #2d constraint (numbered from 0) deleted >>> prob.constraints [# (1x1)affine constraint: 〈 1  x[0] 〉 < y[0] #, # (1x1)affine constraint: 〈 1  x[2] 〉 < y[2] #, # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] #, # (4x1)affine constraint: y > 0 #, # (2x1)affine constraint: x[0] < 2.0 #, # (2x1)affine constraint: x[1] < 2.0 #, # (2x1)affine constraint: x[2] < 2.0 #, # (2x1)affine constraint: x[3] < 1 #] >>> prob.remove_constraint((1,)) #2d 'group' of constraint deleted, i.e. the single constraint y>0 >>> prob.constraints [# (1x1)affine constraint: 〈 1  x[0] 〉 < y[0] #, # (1x1)affine constraint: 〈 1  x[2] 〉 < y[2] #, # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] #, # (2x1)affine constraint: x[0] < 2.0 #, # (2x1)affine constraint: x[1] < 2.0 #, # (2x1)affine constraint: x[2] < 2.0 #, # (2x1)affine constraint: x[3] < 1 #] >>> prob.remove_constraint((2,)) #3d 'group' of constraint deleted, (originally the 4th group, i.e. x[3]<1) >>> prob.constraints [# (1x1)affine constraint: 〈 1  x[0] 〉 < y[0] #, # (1x1)affine constraint: 〈 1  x[2] 〉 < y[2] #, # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] #, # (2x1)affine constraint: x[0] < 2.0 #, # (2x1)affine constraint: x[1] < 2.0 #, # (2x1)affine constraint: x[2] < 2.0 #] >>> prob.remove_constraint((1,1)) #2d constraint of the 2d group (originally the 3rd group), i.e. x[1]<2 >>> prob.constraints [# (1x1)affine constraint: 〈 1  x[0] 〉 < y[0] #, # (1x1)affine constraint: 〈 1  x[2] 〉 < y[2] #, # (1x1)affine constraint: 〈 1  x[3] 〉 < y[3] #, # (2x1)affine constraint: x[0] < 2.0 #, # (2x1)affine constraint: x[2] < 2.0 #]
 if

remove_variable
(name)¶ Removes the variable
name
from the problem. :param name: name of the variable to remove. :type name: str.Warning
This method does not check if some constraint still involves the variable to be removed.

reset_cplex_instance
(onlyvar=True)¶ reset the variables used by the solver cplex

reset_cvxopt_instance
(onlyvar=True)¶ reset the variable
cvxoptVars
, used by the solver cvxopt (and smcp)

reset_glpk_instance
(onlyvar=True)¶ reset the variables used by the solver glpk

reset_gurobi_instance
(onlyvar=True)¶ reset the variables used by the solver gurobi

reset_mosek_instance
(onlyvar=True)¶ reset the variables used by the solver mosek

reset_scip_instance
(onlyvar=True)¶ reset the variables used by the solver scip

set_all_options_to_default
()¶ set all the options to their default. The following options are available, and can be passed as pairs of the form
key=value
when theProblem
object is created, or to the functionsolve()
:General options common to all solvers:
verbose = 1
: Verbosity level. 1 attempts to suppress all output, even errors. 0 only outputs warnings and errors. 1 generates standard informative output. 2 prints all available information for debugging purposes.solver = None
: currently the available solvers are'cvxopt'
,'glpk'
,'cplex'
,'mosek'
,'gurobi'
,'smcp'
,'zibopt'
. The defaultNone
means that you let picos select a suitable solver for you.tol = 1e8
: Relative gap termination tolerance for interiorpoint optimizers (feasibility and complementary slackness). This option is currently ignored by glpk.maxit = None
: maximum number of iterations (for simplex or interiorpoint optimizers). This option is currently ignored by zibopt.lp_root_method = None
: algorithm used to solve continuous LP problems, including the root relaxation of mixed integer problems. The defaultNone
selects automatically an algorithm. If set topsimplex
(resp.dsimplex
,interior
), the solver will use a primal simplex (resp. dual simplex, interiorpoint) algorithm. This option currently works only with cplex, mosek and gurobi. With glpk it works for LPs but not for the MIP root relaxation.lp_node_method = None
: algorithm used to solve subproblems at nodes of the branching trees of mixed integer programs. The defaultNone
selects automatically an algorithm. If set topsimplex
(resp.dsimplex
,interior
), the solver will use a primal simplex (resp. dual simplex, interiorpoint) algorithm. This option currently works only with cplex, mosek and gurobi.timelimit = None
: time limit for the solver, in seconds. The defaultNone
means no time limit. This option is currently ignored by cvxopt and smcp.treememory = None
: size of the buffer for the branch and bound tree, in Megabytes. This option currently works only with cplex.gaplim = 1e4
: For mixed integer problems, the solver returns a solution as soon as this value for the gap is reached (relative gap between the primal and the dual bound).noprimals = False
: ifTrue
, do not copy the optimal variable values in thevalue
attribute of the problem variables.noduals = False
: ifTrue
, do not try to retrieve the dual variables.nbsol = None
: maximum number of feasible solution nodes visited when solving a mixed integer problem.hotstart = False
: ifTrue
, the MIP optimizer tries to start from the solution specified (even partly) in thevalue
attribute of the problem variables. This option currently works only with cplex, mosek and gurobi.convert_quad_to_socp_if_needed = True
: Do we convert the convex quadratics to second order cone constraints when the solver does not handle them directly ?solve_via_dual = None
: If set toTrue
, the Lagrangian Dual (computed with the functiondualize()
) is passed to the solver, instead of the problem itself. In some situations this can yield an important speedup. In particular for Mosek and SOCPs/SDPs whose form is close to the standard primal form (as in the note on dual variables of the tutorial), since the MOSEK interface is better adapted for problems given in a dual form. When this option is set toNone
(default), PICOS chooses automatically whether the problem itself should be passed to the solver, or rather its dual.pass_simple_cons_as_bound = False
: If set toTrue
, linear constraints involving a single variable are passed to the solvers as a bound on the variable. This may speedup the solving process (?), but is not safe if you indend to remove this constraint later and resolve the problem. This option currently works only with cplex, mosek and gurobi.return_constraints = False
: If set toTrue
, the default behaviour of the functionadd_constraint()
is to return the created constraint.
Specific options available for cvxopt/smcp:
feastol = None
: feasibility tolerance passed to cvx.solvers.options Iffeastol
has the default valueNone
, then the value of the optiontol
is used.abstol = None
: absolute tolerance passed to cvx.solvers.options Ifabstol
has the default valueNone
, then the value of the optiontol
is used.reltol = None
: relative tolerance passed to cvx.solvers.options Ifreltol
has the default valueNone
, then the value of the optiontol
, multiplied by10
, is used.
Specific options available for cplex:
cplex_params = {}
: a dictionary of cplex parameters to be set before the cplex optimizer is called. For example,cplex_params={'mip.limits.cutpasses' : 5}
will limit the number of cutting plane passes when solving the root node to5
.acceptable_gap_at_timelimit = None
: If the the time limit is reached, the optimization process is aborted only if the current gap is less than this value. The default valueNone
means that we interrupt the computation regardless of the achieved gap.uboundlimit = None
: tells CPLEX to stop as soon as an upper bound smaller than this value is found.lboundlimit = None
: tells CPLEX to stop as soon as a lower bound larger than this value is found.boundMonitor = True
: tells CPLEX to store information about the evolution of the bounds during the solving process. At the end of the computation, a list of triples(time,lowerbound,upperbound)
will be provided in the fieldbounds_monitor
of the dictionary returned bysolve()
.
Specific options available for mosek:
mosek_params = {}
: a dictionary of mosek parameters to be set before the mosek optimizer is called. For example,mosek_params={'simplex_abs_tol_piv' : 1e4}
sets the absolute pivot tolerance of the simplex optimizer to1e4
.handleBarVars = True
: For semidefinite programming, Mosek handles the Linear Matrix Inequalities by using a separate class of variables, called bar variables, representing semidefinite positive matrices.If this option is set to
False
, Mosek adds a new bar variable for every LMI, and let the elements of the slack variable of the LMIs match the bar variables by adding equality constraints.If set to
True
(default), PICOS avoid creating useless bar variables for LMIs of the formX >> 0
: in this caseX
will be added in mosek directly as abar variable
. This can avoid creating a lot of unnecessary variables for problems whose form is close to the canonical dual form (See the note on dual variables in the tutorial).See also the option
solve_via_dual
.handleConeVars = True
: For Second Order Cone Programming, Mosek handles the SOC inequalities by appending a standard cone. This must be done in a careful way, since a single variable is not allowed to belong to several standard cones.If this option is set to
False
, Picos adds a new variable for each coordinate of a vector in a second order cone inequality, as well as an equality constraint to match the value of this coordinate with the value of the new variable.If set to
True
, additional variables are added only when needed, and simple changes of variables are done in order to reduce the number of necessary additional variables. This can avoid creating a lot of unnecessary variables for problems whose form is close to the canonical dual form (See the note on dual variables in the tutorial). Consider for example the SOC inequality . Here 2 new variables and will be added, with the constraint and , and a change of variable will be done. Then a standard cone with the variables will be appended (cf. the doc of the mosek interface).See also the option
solve_via_dual
.
Specific options available for gurobi:
gurobi_params = {}
: a dictionary of gurobi parameters to be set before the gurobi optimizer is called. For example,gurobi_params={'NodeLimit' : 25}
limits the number of nodes visited by the MIP optimizer to 25.
Specific options available for scip:
scip_params = {}
: a dictionary of scip parameters to be set before the scip optimizer is called. For example,scip_params = {'lp/threads' : 4}
sets the number of threads to solve the LPs at 4.
Specific options available for sdpa:
sdpa_executable = 'sdpa'
: The sdpa executable name.sdpa_params = {'pt': 0}
: dictionary of extra parameters to pass to sdpa. Set ‘read_solution’: ‘filename.out’ for reading an already existing solution.

set_objective
(typ, expr)¶ Defines the objective function of the problem.
Parameters:  typ (str.) – can be either
'max'
(maximization problem),'min'
(minimization problem), or'find'
(feasibility problem).  expr – an
Expression
. The expression to be minimized or maximized. This parameter will be ignored iftyp=='find'
.
 typ (str.) – can be either

set_option
(key, val)¶ Sets the option key to the value val.
Parameters:  key (str.) – The key of an option
(see the list of keys in the doc of
set_all_options_to_default()
).  val – New value for the option.
 key (str.) – The key of an option
(see the list of keys in the doc of

set_var_value
(name, value, optimalvar=False)¶ Sets the
value
attribute of the given variable.Parameters:  name (str.) – name of the variable to which the value will be given
 value – The value to be given. The type of
value
must be recognized by the function_retrieve_matrix()
, so that it can be parsed into acvxopt sparse matrix
of the desired size.
Example
>>> prob=pic.Problem() >>> x=prob.add_variable('x',2) >>> prob.set_var_value('x',[3,4]) #this is in fact equivalent to x.value=[3,4] >>> abs(x)**2 #quadratic expression: x**2 # >>> print (abs(x)**2) 25.0

solve
(**options)¶ Solves the problem.
Once the problem has been solved, the optimal variables can be obtained thanks to the property
value
of the classExpression
. The optimal dual variables can be accessed by the propertydual
of the classConstraint
.Parameters: options – A list of options to update before the call to the solver. In particular, the solver can be specified here, under the form key = value
. See the list of available options in the doc ofset_all_options_to_default()
Returns: A dictionary which contains the objective value of the problem, the time used by the solver, the status of the solver, and an object which depends on the solver and contains information about the solving process.

solver_selection
()¶ Selects an appropriate solver for this problem and sets the option
'solver'
.

status
= None¶ status returned by the solver. The default when a new problem is created is ‘unsolved’.

to_real
()¶ Returns an equivalent problem, where the n x n hermitian matrices have been replaced by symmetric matrices of size 2n x 2n.

type
¶ Type of Optimization Problem (‘LP’, ‘MIP’, ‘SOCP’, ‘QCQP’,…)

update_options
(**options)¶ update the option dictionary, for each pair of the form
key = value
. For a list of available options and their default values, see the doc ofset_all_options_to_default()
.

variables
= None¶ dictionary of variables indexed by variable names

write_to_file
(filename, writer='picos')¶ This function writes the problem to a file.
Parameters:  filename (str.) –
The name of the file where the problem will be saved. The extension of the file (if provided) indicates the format of the export:
'.cbf'
: CBF (Conic Benchmark Format). This format is suitable for optimization problems involving second order and/or semidefinite cone constraints. This is the standard to use for conic optimization problems, cf. CBLIB and this paper from Henrik Friberg.'.lp'
: LP format . This format handles only linear constraints, unless the writer'cplex'
is used, and the file is saved in the extended cplex LP format'.mps'
: MPS format (recquires mosek, gurobi or cplex).'.opf'
: OPF format (recquires mosek).'.dats'
: sparse SDPA format This format is suitable to save semidefinite programs (SDP). SOC constraints are stored as semidefinite constraints with an arrow pattern.
 writer (str.) – The default writer is
picos
, which has its own LP, CBF, and sparse SDPA write functions. If cplex, mosek or gurobi is installed, the user can pass the optionwriter='cplex'
,writer='gurobi'
orwriter='mosek'
, and the write function of this solver will be used.
Warning
In the case of a SOCP, when the selected writer is
'mosek'
, the written file may contain some changes of variables with respect to the original formulation when the optionhandleConeVars
is set toTrue
(this is the default).If this is an issue, turn the option
handleConeVars
toFalse
and reset the mosek instance by callingreset_mosek_instance()
, but turning off this option may increase the number of variables and constraints.Otherwise, the set of change of variables can be queried by
self.msk_scaledcols
. Each (Key,Value) pairi > alpha
of this dictionary indicates that thei
th column has been rescaled by a factoralpha
.The CBF writer tries to write symmetric variables in the section
PSDVAR
of the .cbf file. However, this is possible only if the constraint appears in the problem, and no other LMI involves . If these two conditions are not satisfied, then the symmetricvectorization of is used as a (free) variable of the sectionVAR
in the .cbf file (cf. next paragraph).For problems involving a symmetric matrix variable (typically, semidefinite programs), the expressions involving are stored in PICOS as a function of , the symmetric vectorized form of X (see Dattorro, ch.2.2.2.1). As a result, the symmetric matrix variables are written in form in the files created by this function. So if you use another solver to solve a problem that is described in a file created by PICOS, the optimal symmetric variables returned will also be in symmetric vectorized form.
 filename (str.) –
