56 datasets found

The CEC2022 basic benchmark problems dataset, which includes 5 problems from the CEC2022 benchmark suite.

The dataset used in this paper is a stochastic multi-level composition optimization problem.

The dataset used in the paper is a ride-hailing service problem, where N companies compete to put the most vehicles on the road to attract the most customers.

The dataset used in the paper is a non-convex optimization problem, specifically the Rastrigin function and the Griewank function.

The dataset is used to analyze the convergence of Riemannian stochastic gradient descent (RSGD) on Riemannian manifolds.

The gasoline blending scheduling problem is a multiobjective optimization problem that involves resource allocation and operation sequencing to meet a refinery's production...

The Branin function is a well-known function for benchmarking optimization methods. It is a 2D function with three global maxima.

A synthetic dataset created for the purpose of training a generative model for optimal geometry generation.

Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance...

The dataset used in the paper is a least squares regression problem instance.

The dataset used in this paper is the Cost Function Library benchmark.

The dataset used in this paper is the Rosenbrock function and the Rastrigin function.

The Adam dataset is a dataset used for training Adam models.

The dataset used in the paper is a complex-valued momentum for optimization in games.

The dataset used in the paper is not explicitly mentioned, but it is mentioned that the authors used Rosenbrock function for optimization.

The dataset used in this paper is a continuous-time stochastic control problem for SGD and similar stochastic gradient descent algorithms.