picos¶

The picos namespace gives you quick access to the most important classes and functions for optimizing with PICOS, so that import picos is often sufficient for implementing your model. You can find additional utilities in the picos.tools namespace.

Functions¶

`ascii`()	Let PICOS create future string representations using only ASCII characters.
`ball`(r[, p])	returns a `Ball` object representing:
`default_charset`([rebuildDerivedGlyphs])	Let PICOS create future string representations using unicode characters.
`detrootn`(exp)	returns a `DetRootN_Exp` object representing the determinant of the $n$ th-root of the symmetric matrix `exp`, where $n$ is the dimension of the matrix.
`diag`(exp[, dim])	if `exp` is an affine expression of size (n,m), `diag(exp,dim)` returns a diagonal matrix of size `dimnm` $\times$ `dimnm`, with `dim` copies of the vectorized expression `exp[:]` on the diagonal.
`diag_vect`(exp)	Returns the vector with the diagonal elements of the matrix expression `exp`
`exp`(x)	Exponentiation of a PICOS, CVXOPT, NumPy, or numeric expression.
`expcone`()	returns a `ExponentialCone` object representing the set $\operatorname{closure}\ \{(x,y,z): y>0, y\exp(x/y)\leq z \}$
`flow_Constraint`(G, f, source, sink, flow_value)	Constructs a network flow constraint.
`geomean`(exp)	returns a `GeoMeanExp` object representing the geometric mean of the entries of `exp[:]`.
`get_version_info`()
`import_cbf`(filename)	Imports the data from a CBF file, and creates a `Problem` object.
`kron`(A, B)	Kronecker product of 2 expression, at least one of which must be constant
`kullback_leibler`(x[, y])	A shorthand for `KullbackLeibler`.
`lambda_max`(exp)	largest eigenvalue of a square matrix expression (cf.
`lambda_min`(exp)	smallest eigenvalue of a square matrix expression (cf.
`latin1`([rebuildDerivedGlyphs])	Let PICOS create future string representations using ISO 8859-1 characters.
`log`(x)	The logarithm of a PICOS, CVXOPT, NumPy, or numeric expression.
`logsumexp`(exp)	A shorthand for `LogSumExp`.
`lse`(exp)	A shorthand for `LogSumExp`.
`new_param`(name, value)	Declare a parameter for the problem, that will be stored as a `cvxopt sparse matrix`.
`norm`(exp[, num, denom])	returns a `NormP_Exp` object representing the (generalized-) p-norm of the entries of `exp[:]`.
`partial_trace`(X[, k, dim])	Partial trace of an Affine Expression, with respect to the k``th subsystem or to every subsystems in ``k for a tensor product of dimensions `dim`.
`partial_transpose`(exp[, dims_1, subsystems, …])	Partial transpose of an Affine Expression, with respect to given subsystems.
`simplex`([gamma])	returns a `TruncatedSimplex` object representing the set $\{x\geq 0: \|\|x\|\|_1 \leq \gamma \}$ .
`sum`(lst[, it, indices])	This is a replacement for Python’s `sum` that produces sensible string representations when summing PICOS expressions.
`sum_k_largest`(exp, k)	returns a `Sum_k_Largest_Exp` object representing the sum of the `k` largest elements of an affine expression `exp`.
`sum_k_largest_lambda`(exp, k)	returns a `Sum_k_Largest_Exp` object representing the sum of the `k` largest eigenvalues of a square matrix affine expression `exp`.
`sum_k_smallest`(exp, k)	returns a `Sum_k_Smallest_Exp` object representing the sum of the `k` smallest elements of an affine expression `exp`.
`sum_k_smallest_lambda`(exp, k)	returns a `Sum_k_Smallest_Exp` object representing the sum of the `k` smallest eigenvalues of a square matrix affine expression `exp`.
`sumexp`(x[, y])	A shorthand for `SumExponential`.
`trace`(exp)	trace of a square AffinExp
`tracepow`(exp[, num, denom, coef])	Returns a `TracePow_Exp` object representing the trace of the pth-power of the symmetric matrix `exp`, where `exp` is an `AffinExp` which we denote by $X$ .
`truncated_simplex`([gamma, sym])	returns a `TruncatedSimplex` object representing the set:
`unicode`([rebuildDerivedGlyphs])	Let PICOS create future string representations using unicode characters.

Classes¶

`DualizationError`(msg)	Exception raised when a non-standard conic problem is being dualized.
`NonConvexError`(msg)	Exception raised when non-convex quadratic constraints are passed to a solver which cannot handle them.
`NotAppropriateSolverError`(msg)	Exception raised when trying to solve a problem with a solver which cannot handle it
`Problem`(**options)	PICOS’ representation of an optimization problem.
`QuadAsSocpError`(msg)	Exception raised when the problem can not be solved in the current form, because quad constraints are not handled.
`new_problem`	alias of `picos.problem.Problem`

Variables¶

`LOCATION`	str(object=’‘) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
`VERSION_FILE`	str(object=’‘) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str