Bounds for elementwise multiplication: x * y. Uses interval arithmetic: the result is [min(products), max(products)] where products are all c

The name of the solver.

The name of the solver.

Negate an operator. Parameters ---------- expr : LinOp The operator to be negated. Returns ------- LinOp The

\returns the number of non zero coefficients */

Returns the scalar value of the objective given x.

These tests construct two matrices that commute (imposing all eigenvectors equal) and then use the fact that: T=Dop(A, B) for (A, B,

\returns the rank of the matrix of which *this is the complete orthogonal * decomposition. * * \note This method has to determine which pivot

Select and reorder rows from each parameter slice. Semantics match SciPy: new_tensor[i, :] = old_tensor[rows[i], :] This mea

* Returns an N x N sparse identity matrix. */

Converts a negative index into a positive index. Args: index: The index to convert. Can be None. dim: The length of the dimension

Wrapper around PyUnicode_AsUTF8AndSize - call Py_XDECREF on the returned pbytes when finished with the returned string */

Check if all values are inf, sparse-aware (O(nnz) for sparse).

Computes the product of the elements in indices based on the dictionary idx_dict. Parameters ---------- indices : iterable

Convert sparse matrix to dense numpy array and expand to shape if given. Parameters ---------- arr : array-like The array to dens

Generate a batch of symmetric matrices with shape (*batch_shape, n, n).

Element-wise maximum, handling sparse matrices. Uses scipy sparse's .maximum() when either operand is sparse, avoiding explicit densification

Element-wise minimum, handling sparse matrices. Uses scipy sparse's .minimum() when either operand is sparse, avoiding explicit densification

\internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 or 32 bytes alignment depending on the requirements. * On allocatio

Converts a DCP problem to a conic form.

Returns a new problem and data for inverting the new solution. Returns ------- tuple (dict of arguments needed fo

Upcast 0D and 1D to 2D.

Add the block to a slice of the matrix. Args: matrix: The matrix the block will be added to. block: The matrix/scalar

\returns the number of columns. \sa rows() */

\returns true if \a V (full or thin) is asked for in this SVD decomposition */

Compute CSR/CSC-style indptr directly from COO indices. This avoids creating scipy sparse matrices just to get the indptr array. Returns

Multiply by a convolution operator. arameters ---------- lin_op : LinOp The root linear operator. rh_val : NDArray Th

Element-wise multiplication of two CooTensors. For parametrized case: lhs has param_size > 1, rhs has param_size = 1. Each lhs slice is

Diagonal matrix to vector. Given (A, b) in view, select the rows corresponding to the elements on the diagonal in the original expres

min 3 * x[0] + 2 * x[1] + x[2] s.t. 0.1 <= x[0] + x[1] + x[2] <= 1 x >= 0 and ... x[0]

Validate expression gradient for batched symmetric matrix inputs. For shape (*batch, n, n), perturbs each (n, n) slice symmetrically.

Finds the contraction for a given set of input and output sets. Parameters ---------- positions : iterable Integer positions

Returns a new id and updates the id counter. Returns ------- int A new id.

Compute dimensions for ND matmul C @ X. Parameters ---------- const_shape : tuple Shape of the constant C var_shape : tu

Compute dimensions for ND rmul X @ C. Parameters ---------- var_shape : tuple Shape of the variable X const_shape : tupl

Map CPLEX status to CPXPY status.

Create status map from Xpress to CVXPY

inline Index row() const { return (ArgType::Flags&RowMajorBit ? Base::outer() : this->index()); } inline Index col() const { return (ArgType::Flags&Ro