diff --git a/CHANGELOG.md b/CHANGELOG.md index 74aa624..34b7fe7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), ## [Unreleased] +## [0.3.0] - 2026-01-29 + ### Changed - Switched back from `@repl` blocks to `jldoctest` blocks in the docstrings, since *Documenter.jl* stopped executing `@repl` blocks in the generated documentation (#217). @@ -156,7 +158,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), - Released the initial stable version of the package. -[unreleased]: https://github.com/Luis-Varona/MatrixBandwidth.jl/compare/v0.2.3...HEAD +[unreleased]: https://github.com/Luis-Varona/MatrixBandwidth.jl/compare/v0.3.0...HEAD +[0.3.0]: https://github.com/Luis-Varona/MatrixBandwidth.jl/releases/tag/v0.3.0 [0.2.3]: https://github.com/Luis-Varona/MatrixBandwidth.jl/releases/tag/v0.2.3 [0.2.2]: https://github.com/Luis-Varona/MatrixBandwidth.jl/releases/tag/v0.2.2 [0.2.1]: https://github.com/Luis-Varona/MatrixBandwidth.jl/releases/tag/v0.2.1 diff --git a/Project.toml b/Project.toml index ed8b849..e0705a7 100644 --- a/Project.toml +++ b/Project.toml @@ -2,7 +2,7 @@ name = "MatrixBandwidth" uuid = "075aa41b-24fd-4573-b25f-92cae432c0f8" keywords = ["matrix bandwidth", "sparse matrices", "optimization", "scientific computing", "julia"] license = "MIT" -version = "0.2.3" +version = "0.3.0" authors = ["Luis M. B. Varona "] description = "Fast algorithms for matrix bandwidth minimization and recognition, written in Julia." homepage = "https://luis-varona.github.io/MatrixBandwidth.jl/" diff --git a/README.md b/README.md index e5a261b..88d4ebd 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ Metadata - Version + Version License: MIT Code Style: Blue @@ -329,6 +329,6 @@ We welcome all bug reports, feature requests, and contributions! Please refer to ## Project status -The latest stable release of MatrixBandwidth.jl is v0.2.3. Although several metaheuristic algorithms are still under development, the rest of the package is fully functional and covered by unit tests. +The latest stable release of MatrixBandwidth.jl is v0.3.0. Although several metaheuristic algorithms are still under development, the rest of the package is fully functional and covered by unit tests. Currently, MatrixBandwidth.jl's core functions generically accept any input of the type `AbstractMatrix{<:Number}`, not behaving any differently when given sparsely stored matrices (e.g., from the [SparseArrays.jl](https://github.com/JuliaSparse/SparseArrays.jl) standard library package). Capabilities for directly handling graph inputs (aiming to reduce the matrix bandwidth of a graph's adjacency) are also not available. Given that bandwidth reduction is often applied to sparse matrices and graphs, these limitations will be addressed in a future release of the package. diff --git a/docs/src/index.md b/docs/src/index.md index dba5570..4dd108c 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -16,7 +16,7 @@ CurrentModule = MatrixBandwidth Metadata - Version + Version License: MIT Code Style: Blue @@ -237,7 +237,7 @@ We welcome all bug reports, feature requests, and contributions! Please refer to ## Project status -The latest stable release of MatrixBandwidth.jl is v0.2.3. Although several metaheuristic algorithms are still under development, the rest of the package is fully functional and covered by unit tests. +The latest stable release of MatrixBandwidth.jl is v0.3.0. Although several metaheuristic algorithms are still under development, the rest of the package is fully functional and covered by unit tests. Currently, MatrixBandwidth.jl's core functions generically accept any input of the type `AbstractMatrix{<:Number}`, not behaving any differently when given sparsely stored matrices (e.g., from the [SparseArrays.jl](https://github.com/JuliaSparse/SparseArrays.jl) standard library package). Capabilities for directly handling graph inputs (aiming to reduce the matrix bandwidth of a graph's adjacency) are also not available. Given that bandwidth reduction is often applied to sparse matrices and graphs, these limitations will be addressed in a future release of the package.