A Julia Linear Operator Package
| Documentation | Linux/macOS/Windows/FreeBSD | Coverage | DOI | 
|---|---|---|---|
If you use LinearOperators.jl in your work, please cite using the format given in CITATION.cff.
Operators behave like matrices (with some exceptions - see below) but are defined by their effect when applied to a vector. They can be transposed, conjugated, or combined with other operators cheaply. The costly operation is deferred until multiplied with a vector.
Julia 1.6 and up.
pkg> add LinearOperators
pkg> test LinearOperatorsCheck the tutorial.
| Operator | Description | 
|---|---|
| LinearOperator | Base class. Useful to define operators from functions | 
| TimedLinearOperator | Linear operator instrumented with timers from TimerOutputs | 
| BlockDiagonalOperator | Block-diagonal linear operator | 
| opEye | Identity operator | 
| opOnes | All ones operator | 
| opZeros | All zeros operator | 
| opDiagonal | Square (equivalent to diagm()) or rectangular diagonal operator | 
| opInverse | Equivalent to \ | 
| opCholesky | More efficient than opInversefor symmetric positive definite matrices | 
| opHouseholder | Apply a Householder transformation I-2hh' | 
| opHermitian | Represent a symmetric/hermitian operator based on the diagonal and strict lower triangle | 
| opRestriction | Represent a selection of "rows" when composed on the left with an existing operator | 
| opExtension | Represent a selection of "columns" when composed on the right with an existing operator | 
| LBFGSOperator | Limited-memory BFGS approximation in operator form (damped or not) | 
| InverseLBFGSOperator | Inverse of a limited-memory BFGS approximation in operator form (damped or not) | 
| LSR1Operator | Limited-memory SR1 approximation in operator form | 
| Function | Description | 
|---|---|
| check_ctranspose | Cheap check that A'is correctly implemented | 
| check_hermitian | Cheap check that A = A' | 
| check_positive_definite | Cheap check that an operator is positive (semi-)definite | 
| diag | Extract the diagonal of an operator | 
| Matrix | Convert an abstract operator to a dense array | 
| hermitian | Determine whether the operator is Hermitian | 
| push! | For L-BFGS or L-SR1 operators, push a new pair {s,y} | 
| reset! | For L-BFGS or L-SR1 operators, reset the data | 
| show | Display basic information about an operator | 
| size | Return the size of a linear operator | 
| symmetric | Determine whether the operator is symmetric | 
| normest | Estimate the 2-norm | 
| solve_shifted_system! | Solves linear system | 
Operators can be transposed (transpose(A)), conjugated (conj(A)) and conjugate-transposed (A').
Operators can be sliced (A[:,3], A[2:4,1:5], A[1,1]), but unlike matrices, slices always return
operators (see differences below).
Unlike matrices, an operator never reduces to a vector or a number.
A = rand(5,5)
opA = LinearOperator(A)
A[:,1] * 3 # Vector
opA[:,1] * 3 # LinearOperator
A[:,1] * [3] # ERROR
opA[:,1] * [3] # VectorThis is also true for A[i,J], which returns vectors on 0.5, and for the scalar
A[i,j].
Similarly, opA[1,1] is an operator of size (1,1):"
opA[1,1] # LinearOperator
A[1,1] # NumberIn the same spirit, the operator full always returns a matrix.
full(opA[:,1]) # nx1 matrix- LimitedLDLFactorizations features a limited-memory LDLT factorization operator that may be used as preconditioner in iterative methods
- MUMPS.jl features a full distributed-memory factorization operator that may be used to represent the preconditioner in, e.g., constraint-preconditioned Krylov methods.
If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.
If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers organization, so questions about any of our packages are welcome.