Nec corporation (20240095610). OPTIMIZATION APPARATUS, OPTIMIZATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING OPTIMIZATION PROGRAM simplified abstract
Contents
- 1 OPTIMIZATION APPARATUS, OPTIMIZATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING OPTIMIZATION PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OPTIMIZATION APPARATUS, OPTIMIZATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING OPTIMIZATION PROGRAM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
OPTIMIZATION APPARATUS, OPTIMIZATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING OPTIMIZATION PROGRAM
Organization Name
Inventor(s)
OPTIMIZATION APPARATUS, OPTIMIZATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING OPTIMIZATION PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095610 titled 'OPTIMIZATION APPARATUS, OPTIMIZATION METHOD AND NON-TRANSITORY COMPUTER-READABLE MEDIUM STORING OPTIMIZATION PROGRAM
Simplified Explanation
The present invention relates to an optimization apparatus for achieving highly accurate optimization for a submodular function without the need for preparing data for machine learning. The apparatus includes a determination unit, a reward acquisition unit, a calculation unit, and an update unit to optimize policies in an objective function with diminishing marginal utility and convexity.
- Determination unit: Identifies one or more execution policies from a predetermined policy in the objective function.
- Reward acquisition unit: Obtains the reward, which is the execution result in the objective function for the determined execution policy.
- Calculation unit: Computes the update rate of the policy based on the reward.
- Update unit: Adjusts the policy based on the calculated update rate.
Potential Applications
This technology can be applied in various fields such as finance, logistics, resource allocation, and recommendation systems.
Problems Solved
1. Achieving highly accurate optimization for submodular functions without the need for data preparation for machine learning. 2. Optimizing policies in an objective function with diminishing marginal utility and convexity.
Benefits
1. Improved accuracy in optimization tasks. 2. Efficient policy optimization without the requirement of extensive data preparation. 3. Enhanced performance in various applications.
Potential Commercial Applications
"Optimization Apparatus for Submodular Functions: Enhancing Policy Optimization Efficiency"
Possible Prior Art
There may be prior art related to optimization techniques for submodular functions and policy optimization in convex objective functions. Research in the fields of machine learning, optimization, and artificial intelligence may have similar approaches to policy optimization without data preparation.
Unanswered Questions
=== How does this technology compare to traditional machine learning approaches for policy optimization? This article does not provide a direct comparison between this technology and traditional machine learning methods for policy optimization. It would be interesting to know the specific advantages and disadvantages of this approach compared to more conventional techniques.
=== What are the potential limitations or challenges in implementing this optimization apparatus in real-world applications? The article does not address any potential limitations or challenges that may arise when implementing this technology in practical scenarios. Understanding the constraints or difficulties in deploying this apparatus could provide valuable insights for potential users or developers.
Original Abstract Submitted
a purpose of the present invention is to achieve highly accurate optimization for a submodular function when a policy is optimized without preparing data for machine learning. an optimization apparatus () according to the present invention includes a determination unit () that determines one or more execution policies from a predetermined policy in an objective function having diminishing marginal utility and convexity, a reward acquisition unit () that acquires reward, which is an execution result in the objective function for the determined execution policy, a calculation unit () that calculates an update rate of the policy based on the reward, and an update unit () that updates the policy based on the update rate.
