Moreover, nonlinear optimization can be computationally expensive in terms of time and memory, so care must be taken when matching an algorithm to a problem. All optimization techniques in PROC NLP ...

In this paper we present a nonmonotone trust region algorithm for general nonlinear constrained optimization problems. The main idea of this paper is to combine Yuan's technique[1] with a nonmonotone ...

Again, nonlinear optimization is used with a quadratic-ish OF. Trends and associations: The new era of big data and machine learning seeks to identify trends or relations within data using empirical ...

HOLO's nonlinear quantum optimization algorithm has achieved groundbreaking progress in theoretical research while also showcasing broad prospects across multiple real-world application scenarios.

Therefore, nonlinear optimization algorithms are considered to solve the above problem. We present and experimentally demonstrate a QPI technique for high-throughput artifact-free slightly off-axis ...

To address the problem that multiple interfering factors join the optimization process and lead to large errors in the optimization results, a nonlinear optimization algorithm based on the Cauchy ...

This paper presents a study on the localization of debris from the Seven Rockets using nonlinear optimization algorithms and linear matching. The research first determines the time differences of ...

No algorithm for optimizing general nonlinear functions exists that always finds the global optimum for a general nonlinear minimization problem in a reasonable amount of time. Since no single ...