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Fractal feature selection model for enhancing high-dimensional biological problems
The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode...
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Combination of optimization-free kriging models for high-dimensional problems
Kriging metamodeling (also called Gaussian Process regression) is a popular approach to predict the output of a function based on few observations....
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High-dimensional stochastic control models for newsvendor problems and deep learning resolution
This paper studies continuous-time models for newsvendor problems with dynamic replenishment, financial hedging and Stackelberg competition. These...
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Lax-Oleinik-Type Formulas and Efficient Algorithms for Certain High-Dimensional Optimal Control Problems
Two of the main challenges in optimal control are solving problems with state-dependent running costs and develo** efficient numerical solvers that...
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Decomposition of Convex High Dimensional Aggregative Stochastic Control Problems
We consider the framework of convex high dimensional stochastic control problems, in which the controls are aggregated in the cost function. As first...
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Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems
Many industrial applications require time-consuming and resource-intensive evaluations of suitable solutions within very limited time frames....
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Surrogate-Assisted Hybrid Searching Method for High-Dimensional Expensive Optimization Problems
To address the challenges of intensive computation cost and poor convergence for high-dimensional expensive optimization problems, a...
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Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems
Numerical methods for the optimal feedback control of high-dimensional dynamical systems typically suffer from the curse of dimensionality. In the...
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VI-DGP: A Variational Inference Method with Deep Generative Prior for Solving High-Dimensional Inverse Problems
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main...
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Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems
High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When...
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Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton–Jacobi PDEs
Solving high-dimensional optimal control problems and corresponding Hamilton–Jacobi PDEs are important but challenging problems in control...
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Application of the Godunov Scheme to Solve Three-Dimensional Problems of High-Speed Interactions of Elastoplastic Bodies
AbstractA 3D technique for modeling the high-speed shock-wave interaction of solid deformable bodies with large displacements and deformations in...
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Ultra Fast Classification and Regression of High-Dimensional Problems Projected on 2D
We propose the two-dimensional visual map classifier and regressor, which project the high-dimensional patterns on a 2D map, for human visualization...
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A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems
By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)...
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An Exploitation-Enhanced Bayesian Optimization Algorithm for High-Dimensional Expensive Problems
The Bayesian optimization (BO) algorithm is widely used to solve expensive optimization problems. However, when dealing with high-dimensional...
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Tornado: An Autonomous Chaotic Algorithm for High Dimensional Global Optimization Problems
In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for high dimensional global optimization problems. The...
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High-Dimensional Multi-objective PSO Based on Radial Projection
When solving multi-objective problems, traditional methods face increased complexity and convergence difficulties because of the increasing number of...
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A novel data-driven sparse polynomial chaos expansion for high-dimensional problems based on active subspace and sparse Bayesian learning
Polynomial chaos expansion (PCE) has recently drawn growing attention in the community of stochastic uncertainty quantification (UQ). However, the...
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A two-stage surrogate-assisted meta-heuristic algorithm for high-dimensional expensive problems
This study proposes a two-stage surrogate-assisted meta-heuristic algorithm named SDAMA-SPS to solve computationally expensive problems with high...
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One-Dimensional Variational Problems
This chapter is devoted to one of the most classical classes of variational problems, the one-dimensional case.