David Huckleberry Gutman

David Huckleberry Gutman

Assistant Professor, Texas A&M University ISEN, Optimizer

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

talks

Tangent Subspace Descent on Fixed-Rank Matrix Manifolds

Published:

Locations:

  • International Conference on Continuous Optimization (ICCOPT 2025), Los Angeles, CA
  • INFORMS Annual Meeting 2024, Seattle, WA
  • International Symposium on Mathematical Programming (ISMP 2024), Montréal, QC, Canada
  • Conference in Honor of Panos Pardalos’s 70th Birthday, Halkidiki, Greece

teaching

21-127 - Concepts of Mathematics (Summer 2015 & 2016)

Undergraduate course, Carnegie Mellon University, Department of Mathematical Sciences, 2015

This course introduces the basic concepts, ideas and tools involved in doing mathematics. As such, its main focus is on presenting informal logic, and the methods of mathematical proof. These subjects are closely related to the application of mathematics in many areas, particularly computer science. Topics discussed include a basic introduction to elementary number theory, induction, the algebra of sets, relations, equivalence relations, congruences, partitions, and functions, including injections, surjections, and bijections. A basic introduction to the real numbers, rational and irrational numbers. Supremum and infimum of a set.

IE5331 - Large-Scale Optimization for Data Science

Graduate course, Texas Tech University, IMSE, 2020

This course covers the fundamentals of first- and second-order optimization methods used in modern machine learning and statistics. Topics include: gradient and subgradient descent, the proximal gradient method, mirror descent, the Frank-Wolfe method, stochastic gradient descent, variance reduction, and quasi-newton methods.

IE5331 - PhD Optimization II

Graduate course, Texas Tech University, IMSE, 2021

This is the second in a complete two-course introduction to theory of linear, nonlinear, and combinatorial optimization. We plan to cover all of linear optimization and some of the rudiments of nonlinear optimization in this course. We will emphasize the theory and analysis of optimization problems with a view toward understanding the complexity of key algorithms and problems.

IE5311 - Principles of Optimization (Spring 2023)

Graduate course, Texas Tech University, IMSE, 2023

This course develops the theory and applications of linear optimization. Topics include simplex and revised-simplex algorithms, dual and primal-dual methods, sensitivity analysis, parametric programming, decomposition, complementarity, assignment and transportation models, and Karmarkar’s algorithm.

ISEN320 - Operations Research I (Fall 2023)

Undergraduate course, Texas A&M University, 2023

This course introduces deterministic optimization through the formulation and solution of linear programs, integer programs, and network flow models. Students use optimization software to apply these methods to practical problems.

ISEN629 - Engineering Optimization (Spring 2024)

Graduate course, Texas A&M University, 2024

Convex analysis provides the mathematical framework for studying nonlinear optimization. Topics include cones and convex sets, hyperplane separation, subgradients, convex conjugacy, duality, and the effects of changing model parameters, together with applications of these principles and methods.

ISEN 689 - Advanced Mathematics for Engineering Ph.D.s (Fall 2024 & 2025)

Graduate course, Texas A&M University, 2025

This course covers the basic theory of sets, functions, numbers, linear algebra, and real analysis necessary for graduate coursework in operations research. Engineering PhD students focusing in intensely mathematical or theoretical fields will also find this material to be integral to their work.