Results 41 to 50 of about 18,755 (251)
Orthogonal methods based ant colony search for solving continuous optimization problems [PDF]
, 2008 Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation.
Jun Zhang +5 more
core +1 more source
Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization
AIChE Journal, EarlyView.Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Efficiency of coordinate descent methods on huge-scale optimization problems [PDF]
In this paper we propose new methods for solving huge-scale optimization problems. For problems of this size, even the simplest full-dimensional vector operations are very expensive.
NESTEROV, Yurii
core
Advanced Intelligent Discovery, EarlyView.This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
Approximating Hessians in unconstrained optimization arising from discretized problems [PDF]
, 2011 Unconstrained optimization, Discretized problems, Sparsity, Partial separability, Numerical experiences,
Vincent Malmedy +3 more
core +1 more source
Objective acceleration for unconstrained optimization [PDF]
Numerical Linear Algebra with Applications, 2018 SummaryAcceleration schemes can dramatically improve existing optimization procedures. In most of the work on these schemes, such as nonlinear generalized minimal residual (N‐GMRES), acceleration is based on minimizing the ℓ2 norm of some target on subspaces of .
openaire +3 more sources
Advanced Intelligent Discovery, EarlyView.Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley +1 more source
A Class of Preconditioners for Large Indefinite Linear Systems, as by-product of Krylov subspace Methods: Part II [PDF]
In this paper we consider the parameter dependent class of preconditioners M(a,d,D) defined in the companion paper The latter was constructed by using information from a Krylov subspace method, adopted to solve the large symmetric linear system Ax = b ...
Massimo Roma, Giovanni Fasano
core
Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction
Advanced Intelligent Systems, EarlyView.This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin +3 more
wiley +1 more source