Update

Project.toml

@@ -7,25 +7,25 @@ version = "0.1.0-dev"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 QRupdate = "29d2ae51-7246-454b-9a65-19ff7b2849a5"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
-
 [compat]
-julia = "1.9, 1.10"
-DataFrames = "1.6" 
+DataFrames = "1.6"
+LinearAlgebra = "1.9, 1.10"
 LinearOperators = "2.8"
+Printf = "1.9, 1.10"
 QRupdate = "1.0"
-LinearAlgebra = "1.9, 1.10" 
-Printf = "1.9, 1.10" 
 Random = "1.9, 1.10"
 SparseArrays = "1.9, 1.10"
+Logging = "1.9"
+julia = "1.9, 1.10"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-# Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [targets]
-test = ["Test"]
+test = ["Test"]

README.md

@@ -3,3 +3,56 @@
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://MPF-Optimization-Laboratory.github.io/ActiveSetPursuit.jl/stable/)
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://MPF-Optimization-Laboratory.github.io/ActiveSetPursuit.jl/dev/)
 [![Build Status](https://github.com/MPF-Optimization-Laboratory/ActiveSetPursuit.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/MPF-Optimization-Laboratory/ActiveSetPursuit.jl/actions/workflows/CI.yml?query=branch%3Amain)
+
+
+## ActiveSetPursuit
+
+`ActiveSetPursuit` is a Julia package providing a framework for solving several variations of the sparse optimization problem:
+
+$$\underset{x}{\text{minimize}} \hspace{0.5em}  \lambda \||x\||_1 + \frac{1}{2} \||Ax - b\||^2_2$$
+
+### Implemented Algorithms:
+- Basis Pursuit Denoising
+- Orthogonal Matching Pursuit
+- Homotopy Basis Pursuit Denoising
+
+### Installation:
+
+The package can be installed as follows:
+```jlcon
+julia> ] add ActiveSetPursuit
+
+```
+
+
+
+## Example Usage
+
+The `asp_bpdn` function within the `ActiveSetPursuit` package efficiently solves the basis pursuit denoising problem.
+
+Ensure that the following inputs are defined before running the `asp_bpdn` function:
+- **`A`**: The matrix or operator, size `m`-by-`n`.
+- **`b`**: The vector of observations or measurements, size `m`.
+- **`λin`**: A nonnegative scalar that serves as the regularization parameter.
+
+To solve the basis pursuit denoising problem, execute the following command in Julia:
+
+```julia
+N, M = 1000, 300
+A = randn(N, M)
+xref = zeros(M)
+xref[randperm(M)[1:20]] = randn(20)  # 20 non-zero entries
+b = A * xref
+const λin = 0.0
+tracer = asp_bpdn(A, b, λin)
+```
+After the optimization process completes, the solution vector and the regularization parameter at any iteration `itn` can be accessed as follows:
+
+```jlcon
+xx, λ = tracer[itn]
+```
+To extract the final iterate:
+
+ ```jlcon
+x_final, λ_final = tracer[itn]
+```

src/ActiveSetPursuit.jl

@@ -1,16 +1,17 @@
 module ActiveSetPursuit
 
 using LinearAlgebra, SparseArrays, LinearOperators, Printf
-using QRupdate
 using DataFrames
-using LinearAlgebra, SparseArrays, LinearOperators, Printf
 using QRupdate, Random
-using LinearAlgebra
+using Logging
 
-export bpdual, asp_homotopy
+export bpdual, asp_homotopy, asp_bpdn, asp_omp
 
 include("BPDual.jl")
 include("helpers.jl")
 include("homotopy.jl")
+include("bpdn.jl")
+include("omp.jl")
+
 
 end