Hybrid Gradient-Simulated Annealing Optimization for Neural Network Training
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Solution Overview
Problem
Neural network training in non-convex cost functions often gets stuck in local minima or saddle points, failing to find the global minimum, which is computationally expensive and time-consuming using standard methods.
Innovation Solution
Combining gradient optimization to generate intermediate optimized parameter values and simulated annealing to find final optimized parameter values, where gradient optimization is used to initialize simulated annealing, allowing for efficient exploration of the solution space and reducing computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gradient descent is used to optimize the cost function, then the optimization process is computationally efficient, but the method may find a local minimum or saddle point instead of the global minimum
Solution Approach 1:
The patent applies simulated annealing optimization before gradient descent optimization. The simulated annealing phase performs preliminary exploration of the solution space to identify regions containing global minima, then gradient descent refines these solutions. This preliminary action prevents gradient descent from getting trapped in local minima while maintaining computational efficiency.
2Measurement precision
If simulated annealing is used to find the global minimum, then the optimization accuracy is improved, but the computational cost and time increase significantly
Solution Approach 1:
The patent segments the optimization process into two distinct phases: (1) simulated annealing optimization for global exploration and initial solution identification, and (2) gradient descent optimization for local refinement and convergence. This segmentation allows each method to operate in its optimal regime, achieving both global optimality and computational efficiency.
Solution Approach 2:
Simulated annealing is performed as a preliminary step to guide subsequent gradient descent optimization. By first identifying promising regions through simulated annealing, the computational burden is reduced because gradient descent starts from better initial points and requires fewer iterations to converge.
3Measurement precision
If standard simulated annealing is applied without gradient optimization initialization, then global minima can be found, but the computational expense is high
Solution Approach 1:
Gradient optimization is applied before simulated annealing to generate intermediate optimized parameter values that serve as informed starting points. This preliminary gradient-based initialization reduces the search space for simulated annealing, enabling it to find global minima with fewer iterations and lower computational expense than standard simulated annealing.
Solution Approach 2:
The optimization is divided into a gradient-based phase followed by a simulated annealing phase. The gradient phase quickly reduces the cost function to a reasonable level, and the simulated annealing phase then performs coarse-to-fine search from these improved starting points, reducing overall computational expense compared to running simulated annealing from random initialization.
Data Source
AI summary
A computer-implemented method of training a neural network comprises: identifying, by one or more processors, a set of parameters for a cost function for the neural network; applying, by the one or more processors, a gradient optimization of the cost function to generate a set of intermediate optimized parameter values for the cost function; initializing, by the one or more processors, a simulated annealing optimization of the cost function using the set of intermediate optimized parameter values for the cost function; applying, by the one or more processors, the simulated annealing optimization of the cost function to generate a set of final optimized parameter values for the cost function; assigning, by the one or more processors, the set of final optimized parameter values as weights for connections of the neural network; and using, by the one or more processors, the neural network to perform operations.