The material included in Opt4Deck will be uploaded to GitHub progressively, with each optimization method and its accompanying documentation becoming available as soon as it is finalized. Until the full structure of the platform is complete, the schedule below provides an overview of the planned release sequence for the individual files.

Opt4Deck is an open and freely accessible optimization platform designed for students, researchers, and engineers who require reliable and flexible numerical tools for solving real-world problems. It provides clean, lightweight, and fully documented implementations of four distinct optimization algorithms, covering linear, nonlinear, stochastic, and dynamic formulations. Each method is distributed as an independent Python module and is accompanied by a detailed manual and representative application examples.

The primary goal of Opt4Deck is to offer a practical, dependency-free toolkit that can be used immediately in educational, research, or engineering workflows. The project is built on principles of simplicity, transparency, and seamless integration, enabling users to apply each method without needing specialized theoretical background. By emphasizing proper problem formulation and method configuration, the platform bridges the gap between fundamental optimization theory and effective computational implementation.

Opt4Deck aims to serve as a stable and reliable reference for practitioners in computational optimization, consolidating essential tools, theoretical foundations, and practical guidelines within a single, well-structured environment. Through its clear organization, comprehensive documentation, and easily integrable codebase, the platform supports both academic study and real engineering applications with consistency and clarity.

Opt4Deck brings together essential tools for the practical application of optimization techniques, offering a clear structure, comprehensive documentation, and straightforward usability. Its key features include:

• Four independent optimization algorithms, covering linear, nonlinear, stochastic, and dynamic problem formulations.
• Operation without complex or specialized external libraries, relying solely on standard and widely available Python modules.
• Detailed manuals and accompanying material, supporting both theoretical understanding and proper method configuration.
• Application-ready examples for each algorithm, enabling users to begin working immediately without requiring prior experience in optimization.
• A unified design philosophy, focused on simplicity, transparency, and seamless integration into existing computational workflows.
• Suitability for both educational and research purposes, providing a reliable reference for a wide range of optimization tasks.
• Built-in extensibility, allowing the algorithms to be incorporated into more complex pipelines or adapted to custom use cases.

Opt4Deck incorporates four distinct optimization methods, covering a broad spectrum of problem types ranging from linear and nonlinear formulations to stochastic and dynamic systems. Each algorithm is provided as an independent tool with a clear structure, detailed documentation, and representative application examples.

• SIMPLEX: A classical method for linear programming, suitable for maximizing linear objective functions under linear constraints. It is characterized by stability, transparency, and a reliable convergence process.
• BFGS: An efficient nonlinear optimization algorithm from the Quasi-Newton family, using an iterative Hessian approximation to achieve fast and stable convergence, without requiring analytical gradients.
• Genetic Algorithm: A stochastic evolutionary approach inspired by natural selection. It is well-suited for complex, nonlinear, or multimodal problems where deterministic methods often fail or become trapped in local extrema.
• Adjoint Method: A specialized technique for optimization problems governed by differential equations. It offers highly efficient gradient computation, independent of the number of design variables, making it ideal for dynamic systems and physics-based applications.

The effectiveness of any optimization process depends significantly on how the problem is formulated before applying a computational method. Regardless of the algorithm used, proper preprocessing of the variables, appropriate handling of the objective function, and the correct incorporation of constraints are essential steps toward achieving stable and reliable numerical performance.

• Normalization of Variables: A fundamental aspect of this preparation is the normalization of the design variables. When variables exhibit large differences in scale or magnitude, optimization algorithms often struggle to navigate the search space efficiently. Mapping all variables to a consistent range, such as [0,10], helps improve numerical stability and ensures more balanced variations during the optimization process.
• Transformations of the Objective Function: Equally important is the treatment of the objective function. In many cases, transformations are applied to introduce artificial bounds, making the function smoother or more numerically manageable, or to alter the form of the problem — for example, transforming a maximization task into a minimization one. Such modifications do not change the location of the optimal solution, but they can substantially enhance the performance and convergence behavior of the chosen algorithm.
• Introduction of Constraints: Finally, when a problem involves constraints that are not directly supported by the optimization method, these can be incorporated externally through suitable transformations of the objective function. Techniques such as penalty methods or Lagrange-based formulations allow constrained problems to be solved even with algorithms designed for unconstrained optimization. When applied consistently and smoothly, these transformations provide a practical and flexible way to impose requirements without modifying the underlying algorithm.

The Opt4Deck repository is organized so that each optimization algorithm functions as an independent and clearly structured unit. The layout is intentionally simple and transparent, allowing users to quickly locate the necessary files, consult the documentation, and run the example scripts without any additional configuration. Each folder contains the corresponding Python module, a detailed manual, and a set of example applications, while the core project files are placed at the top level for direct access.

The overall structure of the repository is as follows:

• ./SIMPLEX/ — Contains the Python module implementing the SIMPLEX method, along with a detailed readme, the complete manual, three example applications, and a short 1-minute demonstration video.
• ./BFGS/ — Contains the Python module implementing the BFGS method, together with a detailed readme, the complete manual, three example applications, and a short 1-minute demonstration video.
• ./GENETIC/ — Contains the Python module implementing the Genetic Algorithm, accompanied by a detailed readme, the complete manual, three example applications, and a short 1-minute demonstration video.
• ./ADJOINT/ — Contains the Python module implementing the Adjoint Method, along with a detailed readme, the complete manual, three example applications, and a short 1-minute demonstration video.
• ./TRICKS/ — Supplementary material with useful optimization techniques and practical guidelines.
• ./README.md — The main documentation file of the platform.
• ./LICENSE — The license under which the project is released.
• ./AUTHOR.md — A brief presentation of the creator and contact information.

Installing and using Opt4Deck is straightforward and requires no special configuration beyond the fundamental capabilities of Python. Each optimization method is provided as an independent module, accompanied by its own manual and example applications, allowing users to start working immediately without complex setup procedures.

To use the platform, simply download the repository from GitHub and store it locally. No external or specialized libraries are required; the methods operate using only standard and widely available Python modules, ensuring broad compatibility and easy integration into any computational environment.

Running an algorithm requires only a single call to the module’s main function, following the structure described in its corresponding manual or readme. Users need to provide the necessary inputs — the objective function, the bounds of the design variables, and the required configuration parameters — without needing in-depth knowledge of the underlying theory or implementation details.

Opt4Deck is distributed under the terms specified in the accompanying LICENSE file.

Users are free to use, modify, and distribute the material in accordance with the conditions outlined in that document.

Opt4Deck has been developed by Giannis Serafeim, PhD, Mechanical Engineer, with extensive experience in numerical optimization and computational methods. More information can be found in the accompanying AUTHOR.md file.

Opt4Deck is currently under active development, and a formal scientific reference is planned for publication through the AviXe platform. Until the official paper becomes available, users citing the platform may use the following entry:

      
   @software{Opt4Deck,
      author = {Giannis Serafeim},
      title  = {Opt4Deck: A New Optimization Platform},
      year   = {2025},
      note   = {Available at: https://github.com/Opt4Deck/Opt4Deck}
   }