Variational Autoencoder Latent Space Optimization
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Solution Overview
Problem
Existing optimization techniques face challenges in efficiently acquiring the optimum solution in complex solution spaces, particularly in multimodal spaces where interaction between variables is unknown, leading to increased search times and potential inaccuracies.
Innovation Solution
The use of a variational autoencoder (VAE) is proposed to learn the distribution of training data in a latent space, allowing for the identification of a search range for the optimum solution by analyzing the sparseness or denseness of data distribution, thereby reducing search times and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used in multimodal solution spaces, then the search may cover the entire solution space, but the search time increases significantly and accuracy decreases
Solution Approach 1:
The VAE model performs preliminary learning of the solution space distribution before optimization search. By pre-processing the training data through VAE encoding, the system identifies dense regions in advance, allowing subsequent optimization to focus only on promising areas rather than searching the entire space, thus reducing search time while maintaining accuracy
Solution Approach 2:
The patent replaces traditional mechanical search methods (systematic enumeration, grid search) with a learning-based approach using VAE. The VAE learns the underlying distribution of optimal solutions from training data and guides the search process, substituting brute-force mechanical search with intelligent, distribution-aware exploration that is both faster and more accurate
2Reliability
If the search range is expanded to cover the entire solution space, then the possibility of finding the global optimum increases, but the complexity of the search process increases
Solution Approach 1:
The patent applies local quality by treating different regions of the solution space differently based on their density characteristics. Dense regions are identified as high-priority search areas where optimization resources are concentrated, while sparse regions are either skipped or given minimal attention. This localized search strategy maintains reliability by focusing on promising areas while reducing overall process complexity
Solution Approach 2:
The VAE model performs preliminary analysis of the solution space to identify dense regions before the actual optimization search. This pre-processing step simplifies the subsequent search process by providing a pre-mapped guide of where to search, reducing the complexity of navigating the entire solution space while maintaining the ability to find reliable optima
3Measurement precision
If more training data is used to improve the VAE model accuracy, then the distribution identification becomes more accurate, but the data processing time and computational resources increase
Solution Approach 1:
The VAE model extracts and encodes the essential distribution characteristics of the solution space into a compressed latent representation. By taking out only the critical features and density patterns from the training data rather than processing all raw data repeatedly, the system achieves accurate distribution identification with reduced computational energy requirements
Solution Approach 2:
The VAE creates a compressed copy of the solution space distribution in the latent space. This copied representation captures the essential structure and density information without requiring the full original dataset to be stored or reprocessed, enabling accurate distribution identification while reducing computational and storage energy costs
Data Source
AI summary
A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes learning a variational autoencoder (VAE) by using a plurality of pieces of training data including an objective function; identifying, by inputting the plurality of pieces of training data to the learned VAE, a distribution of the plurality of pieces of training data over a latent space of the learned VAE; determining a search range of an optimum solution of the objective function based on the distribution of the plurality of pieces of training data; and acquiring an optimum solution of a desired objective function by using the pieces of training data included in the search range.