Thursday 10:45 AM–11:30 AM in Track 3 - Hood

Practical Optimization for Stats Nerds

Ryan J. O'Neil

Audience level:
Intermediate

Description

Many models important to inferential statistics and machine learning use some form of optimization under the hood. For example, least squares regression and support vector machines are both implemented as simple optimization models. With the right tools in your hands, optimization can do so much more! This talk shows how to implement familiar statistical models directly using optimization solvers.

Abstract

Practical Optimization for Stats Nerds

Introduction

Format of the talk

This talk takes familiar stats models and explores them from an optimization perspective. It then shows how those same optimization technologies can be used in decision modeling.

Key take-aways

Model 1: Least Squares

Application 1: Portfolio Optimization

Extends the quadratic optimization model used to solve least squares to solve Markowitz portfolio optimization.

Model 2: Support Vector Machines

Interlude: Problem Shapes

What is general problem structure in statistical inferences problems? How does that differ from problem structure of optimization problems? How do we convert these to problems we can solve?

Model 3: Clustering

Application 2: Pedicab Sharing

Demonstrates a realistic approach to solving vehicle routing using integer optimization and Python.

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