Hi there,
This semester, I am taking a course called Operations Research as an EE undergrad who is very interested in learning more about machine learning.
Since machine learning is one of my favorite topics, I want to choose a machine-learning problem for our course project. However, I was not sure what to choose, so I came here looking for help. Our project is to locate a real-world problem and solve and optimize it using linear programming.
Hi Alexander!
Linear programming and operations research have some interesting applications in the field of machine learning. Here are a few examples:
Optimization of Hyperparameters: In machine learning, the performance of models can be significantly affected by the choice of hyperparameters. Linear programming can be used to optimize these hyperparameters efficiently, especially in cases where the objective function and constraints are linear.
Resource Allocation in Training: Operations research methods are often employed to allocate computational resources effectively during the training of machine learning models. This involves determining the optimal way to distribute memory, processing power, and data storage to ensure that models are trained as efficiently as possible.
Feature Selection: Linear programming can assist in feature selection by formulating the selection problem as an optimization task. The goal here is to choose a subset of features that maximizes the performance of the model while minimizing complexity.
Supply Chain Optimization: Operations research techniques are widely used in supply chain management. When integrated with machine learning, these techniques can optimize inventory levels, reduce costs, and improve delivery times by predicting demand and optimizing routes.
Scheduling Problems: In scenarios such as job scheduling, where tasks need to be assigned to resources in the most efficient way, linear programming can be employed to find optimal schedules. Machine learning models can predict task durations and other variables, which are then used in the linear programming model.
Portfolio Optimization: In finance, linear programming is used for portfolio optimization. Machine learning models can predict asset returns, and linear programming can then be used to find the optimal portfolio that maximizes returns while minimizing risk.
These are just a few examples, but they highlight how powerful the combination of linear programming, operations research, and machine learning can be in solving complex real-world problems.
A linear objective function’s ideal values are sought for under a set of linear constraints using the OR model known as linear programming (LP). ML issues like logistic regression, support vector machines, linear regression, and neural network training can all be resolved with LP.