Takes list of tensors which have already been offset along axis 0 (rows) and combines them into a single tensor.

The dimensions of the second-order cones. Returns ------- list A list of the sizes of the elementwise cones.

The dimensions of the exponential cones. Returns ------- list A list of the sizes of the elementwise cones.

The dimensions of the exponential cones. Returns ------- list A list of the sizes of the elementwise cones.

The dimensions of the exponential cones. Returns ------- list A list of the sizes of the elementwise cones.

Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched. * * If the sizes of the matrix are decreased, then the matr

Convert an arbitrary value into a matrix of type self.target_matrix. Args: value: The constant to be converted. conve

Default is empty list of Constants.

Returns the constants in the problem.

Multiplies a vector by the matrix implied by the constraints. Parameters ---------- constraints : list A list of linear constrain

Multiplies a vector by the transpose of the constraints matrix. Parameters ---------- constraints : list A list of linear constra

Unpacks a vector into a list of values for constraints.

Loop through all variables and collect the intial point.

Convert matrix multiplication A @ f(x), f(x) @ A, or X @ Y. Follows numpy's matmul broadcasting rules for 1D operands.

Convert elementwise multiplication.

Check if two coordinate tuples represent the same sparsity pattern. Parameters ---------- coords1 : tuple of array-like First coo

Returns a shallow copy of the GeoMean atom. Parameters ---------- args : list, optional The arguments to reconstr

count. * Return the number of active pointers (i.e, pointers that * have been malloc but not freed). */

Creates an internal equality constraint. Parameters ---------- lh_term : LinOp The left-hand operator in the equality constraint.

Wraps a parameter. Parameters ---------- shape : tuple The (rows, cols) dimensions of the operator. Returns -------

Deindent a list of lines maximally

Create a giant diagonal matrix with entries given by the entries of mat.

Converts a map of variable id to value to a vector. Parameters ---------- val_dict : dict A map of variable id to value. var_

Convert CVXPY cone dimensions to Moreau cone specification. Parameters ---------- cone_dims : ConeDims CVXPY cone dimensions obje

Bounds for elementwise division: x / y. Note: If the divisor interval contains zero, returns unbounded. Parameters ---------- lb1, u

Helper function to extract the arguments from an AddExpression.

min 3 * x[0] + 2 * x[1] + x[2] s.t. 0.1 <= x[0] + x[1] + x[2] <= 1 x >= 0 x[0] >= x[1] * exp(x[2] / x[1])

A random risk-parity portfolio optimization problem.

Validate gradient for expressions with multiple variable arguments. Parameters ---------- expr_factory : callable Function t

Validate expression gradient for symmetric matrix inputs. For symmetric matrices, we perturb in directions that maintain symmetry. This

Extract flattened indices from CVXPY index expression.

Extract flattened indices from CVXPY special_index expression.

\sa MatrixBase::fill() */

\returns the first element \f$ a_0 \f$ in the sequence */

Given a constraint represented as Ax+b in K for K a cvxpy cone, return an instantiated cvxpy constraint.

Labeled representation of the index expression.

Format problem with labels where available. Shows labels for both the objective expression and constraints.

Converts part of a key into a slice with a start and step. Uses the same syntax as numpy. Args: key_val: The value to convert into a

Construct a SolverStats object from a dictionary of attributes. Parameters ---------- attr : dict A dictionary of

Factory function for (weighted) geometric mean. Parameters ---------- x : Expression The value to take the geometric mean of.

Get all constraint IDs from a tree.

Return bounds for this constant. For constants, the bounds are exactly (value, value). Sparse matrices are returned as-is to avoid me

Returns the constant term in the expression. Parameters ---------- lin_op : LinOp The root linear operator. Returns ----

Extract the constant data from a LinOp node of type "*_const".

Get constraint bounds for all constraints. Also converts inequalities to nonneg form, as well as equalities to zero constrain

* Return the coefficients for CONV operator. The coefficient matrix is * constructed by creating a toeplitz matrix with the constant vector * in DAT

Returns the (row slice, column slice).

Returns info needed to reconstruct the object besides the args. Returns ------- list

Returns the default canonicalization backend, which can be set globally using an environment variable.

* Return the coefficients for DIAG_MAT (diagonal matrix to vector): a * N by N^2 matrix where each row has a 1 in the row * N + row entry * correspo

* Return the coefficients for DIAG_VEC (vector to diagonal matrix): a * N^2 by N matrix where each column I has a 1 in row I * N + I * corresponding

* Return the coefficients for DIV: a diagonal matrix where each diagonal * entry is 1 / DIVISOR. * * Parameters: linOp with type DIV * * Returns:

Returns an empty view of the corresponding TensorView subclass, coupling the CanonBackend subclass with the TensorView subclass.

Return an empty CooTensorView.

* Return the coefficients for HSTACK * * Parameters: linOp LIN with type HSTACK * Returns: vector of coefficient matrices for each argument. */

* Return the coefficients for INDEX: a N by ROWS*COLS matrix * where N is the number of total elements in the slice. Element i, j * is 1 if element

Get indices for dimension.

Returns the KKT solver selected by the user. Removes the KKT solver from solver_opts. Parameters ---------- solver_o

* Return the coefficients for KRON_L. * * Parameters: linOp LIN with type KRON_L * Returns: vector containing the coefficient matrix for the Kronec

Get the maximum denominator in a sequence of ``Fraction`` and ``int`` objects

* Return the coefficients for MUL_ELEM: an N x N diagonal matrix where the * n-th element on the diagonal corresponds to the element n = j*rows + i i