• Yogeshri Gaidhani
• Manisha Bharambe
• Nitya Gaidhani

Multiparametric algorithms provide a richer viewpoint in theoretical and applied computer science by analyzing computational behavior through several interacting parameters rather than treating input size as the sole complexity driver. This survey reviews the historical development, emerging trends, and diverse applications of multiparametric methods. It has traced the evolution of this field from its classical roots to the forefront of modern research, highlighting key techniques, application domains, and innovations. Starting from early methods such as kernelization and dynamic programming, the study includes advanced techniques including multiparametric programming, parameter ecology, parallelization, and integration with machine learning. Multiparametric algorithms are used in Chemical Process Optimization, Real time data preprocessing and machine learning. NetworkX and Open Graph Drawing Framework tools which use multiparametric settings are discussed here. Open problems and future directions focusing on parameter selection, complexity bounds, and dynamic adaptability are discussed.

Multiparametric linear programming, Multiparametric non-linear programming, Multiparametric mixed-integer programming, Multiparametric Model Predictive Control, Real time data preprocessing.

"Comprehensive Survey of Multiparametric Algorithms in Computer Science", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.a227-a231, January-2026, Available :http://www.jetir.org/papers/JETIR2601026.pdf

"Comprehensive Survey of Multiparametric Algorithms in Computer Science", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppa227-a231, January-2026, Available at : http://www.jetir.org/papers/JETIR2601026.pdf