Convex Optimization Toolkit for Solving L1-Norm Minimization Problems

This article introduces a convex optimization toolkit specifically designed for solving L1-norm minimization problems. The toolkit incorporates six different model-solving approaches, each with unique advantages and suitable application scenarios. Users can efficiently solve L1-norm minimization problems by leveraging appropriate optimization algorithms and selecting the most suitable method for their specific requirements. The toolkit includes implementations of proximal gradient methods, alternating direction method of multipliers (ADMM), and coordinate descent algorithms with proper regularization handling. Developed by experienced optimization specialists, this toolkit provides high-quality, reliable solutions for practical problem-solving. Key functions include sparse signal recovery, feature selection, and robust regression implementations. We believe this toolkit will serve as an invaluable assistant in your professional work, offering optimized code structures and efficient numerical computation capabilities.