US 2025/0165780 A1Published application

Systems, methods, and media for training neural networks to solve optimatization problems

OwnerXiaodong Wang, Jeremy Johnston +2inventors

InventorsXiaodong Wang, Jeremy Johnston, Xiao-Yang Liu, Shixun Wu

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Abstract

Training neural network(s) (NN(s)) to solve optimization problem (OP) includes: configuring a first NN of the NN(s) with a first set of (FSO) values for NN parameters (NNPs); selecting a FSO training samples (TSs) for training the NN(s); providing the FSO TSs and a FSO first variables to the first NN to produce a FSO first output variables (OVs); evaluating an OP-performance-measuring objective function (OF) based on the FSO TSs and the FSO first OVs to provide a first value of (VO) a performance metric (FVOPM) of the NN(s); updating the NNPs based on the FVOPM; selecting a second set of (SSO) TSs for training the NN(s); providing the SSO TSs and the FSO first OVs to the first NN to produce a SSO first OVs; evaluating the OF based on the SSO TSs and the SSO first OVs to provide a second VO the PM of the NN(s); and updating the NNPs based on the second VO the PM.

Claims21

Dependent claims

A system for training one or more neural networks to solve an optimization problem, comprising:

memory;
and
at least one hardware processor collectively configured to at least:
configure a first neural network of the one or more neural networks with a first set of values for first neural network parameters;
select a first set of training samples for training the one or more neural networks;
provide the first set of training samples and a first set of first variables to the first neural network to produce a first set of first output variables;
evaluate an objective function that measures performance on the optimization problem based on the first set of training samples and the first set of first output variables to provide a first value of a performance metric of the one or more neural networks;
update the first neural network parameters based on the first value of the performance metric;
select a second set of training samples for training the one or more neural networks;
provide the second set of training samples and the first set of first output variables to the first neural network to produce a second set of first output variables;
evaluate the objective function that measures performance on the optimization problem based on the second set of training samples and the second set of first output variables to provide a second value of the performance metric of the one or more neural networks;
and
update the first neural network parameters based on the second value of the performance metric.

10 more elements

A method for training one or more neural networks to solve an optimization problem, comprising:

configuring a first neural network of the one or more neural networks with a first set of values for first neural network parameters;
selecting a first set of training samples for training the one or more neural networks;
providing the first set of training samples and a first set of first variables to the first neural network to produce a first set of first output variables;
evaluating an objective function that measures performance on the optimization problem based on the first set of training samples and the first set of first output variables to provide a first value of a performance metric of the one or more neural networks;
updating the first neural network parameters based on the first value of the performance metric using a hardware processor;
selecting a second set of training samples for training the one or more neural networks;
providing the second set of training samples and the first set of first output variables to the first neural network to produce a second set of first output variables;
evaluating the objective function that measures performance on the optimization problem based on the second set of training samples and the second set of first output variables to provide a second value of the performance metric of the one or more neural networks;
and
updating the first neural network parameters based on the second value of the performance metric.

7 more elements

A non-transitory computer-readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for training one or more neural networks to solve an optimization problem, the method comprising:

configuring a first neural network of the one or more neural networks with a first set of values for first neural network parameters;
selecting a first set of training samples for training the one or more neural networks;
providing the first set of training samples and a first set of first variables to the first neural network to produce a first set of first output variables;
evaluating an objective function that measures performance on the optimization problem based on the first set of training samples and the first set of first output variables to provide a first value of a performance metric of the one or more neural networks;
updating the first neural network parameters based on the first value of the performance metric;
selecting a second set of training samples for training the one or more neural networks;
providing the second set of training samples and the first set of first output variables to the first neural network to produce a second set of first output variables;
evaluating the objective function that measures performance on the optimization problem based on the second set of training samples and the second set of first output variables to provide a second value of the performance metric of the one or more neural networks;
and
updating the first neural network parameters based on the second value of the performance metric.

7 more elements

Dependencies are inferred from the claim text.