ACCELERATOR FOR COMPUTING COMBINATORIAL COST FUNCTION

Organization Name

Inventor(s)

Helmut Gottfried Katzgraber of Kirkland WA (US)

Christopher Anand Pattison of College Station TX (US)

ACCELERATOR FOR COMPUTING COMBINATORIAL COST FUNCTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240202558 titled 'ACCELERATOR FOR COMPUTING COMBINATORIAL COST FUNCTION

The computing device described in the abstract includes memory, an accelerator device, and a processor. The processor generates data packs indicating updates to variables of a combinatorial cost function, which are then transmitted to the accelerator device. The accelerator device retrieves variable values, generates updated values, and calculates updated cost function values using a Monte Carlo algorithm. The final updated cost function value is output to the processor.

Processor generates data packs indicating updates to variables of a combinatorial cost function
Accelerator device retrieves variable values and generates updated values
Accelerator device calculates updated cost function values using a Monte Carlo algorithm
Final updated cost function value is output to the processor
Transition probabilities are determined and stored with the updated values

Potential Applications: - Optimization algorithms - Machine learning models - Financial modeling

Problems Solved: - Efficient updating of variables in combinatorial cost functions - Improved performance of optimization algorithms

Benefits: - Faster computation of updated cost function values - Enhanced accuracy in variable updates - Increased efficiency in optimization processes

Commercial Applications: Title: Accelerated Optimization Algorithm for Financial Modeling This technology can be used in financial institutions for risk management, portfolio optimization, and algorithmic trading. It can also be applied in industries such as logistics for route optimization and resource allocation.

Questions about the technology: 1. How does the use of a Monte Carlo algorithm improve the efficiency of updating variable values in the cost function? 2. What are the potential limitations of this technology in real-world applications?

Frequently Updated Research: Researchers are constantly exploring new ways to optimize the performance of algorithms using accelerator devices and advanced computing techniques. Stay updated on the latest advancements in optimization algorithms and machine learning models for enhanced efficiency and accuracy.

Original Abstract Submitted

a computing device, including memory, an accelerator device, and a processor. the processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. the processor may transmit the plurality of data packs to the accelerator device. the accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. the accelerator device may generate an updated cost function value based on the updated variable value. the accelerator device may be further configured to determine a transition probability using a monte carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. the accelerator device may output a final updated cost function value to the processor.