New Developments in AMPL:
Solver Callbacks, Spreadsheet Interfaces, and Cloud Licensing
IN-PERSON: Tuesday, October 25 2:45-3:30PM PDT
VIRTUAL: Sunday, October 24 8:30-9:15am PDT
Presented by: Robert Fourer, AMPL Optimization Inc.
Built on the concept of model-based optimization, AMPL’s intuitive algebraic modeling language and prototyping environment give you a fast start on prescriptive decision-making projects. AMPL’s APIs for popular programming languages then help you build completed optimization models into your applications. Now AMPL’s APIs also help you get more functionality from widely-used MIP solvers, by providing access to a variety of solver callbacks. This presentation introduces AMPL’s generic solver callback features through two Python notebook examples: implementation of custom-designed solver stopping rules, and dynamic generation of cuts (constraints) during the solution process. Our presentation concludes with summaries of other notable developments in AMPL, including improved interfaces to spreadsheets and databases, and flexible licensing for deployment in virtual environments such as cloud services and containers.
Nonlinear Optimization Using Artelys Knitro
IN-PERSON: Monday, October 25 11-11:45am PDT
VIRTUAL: Sunday, October 24 6:45-7:30am PDT
Presented by: Richard Waltz, Senior Scientist, Artelys Corp, richard.waltz@artelys.com
Nonlinear optimization is used in many applications in a broad range of industries such as economy, finance, energy, health, 3D modeling, and marketing. With four algorithms and great configuration capabilities, Artelys Knitro is the leading solver for nonlinear optimization and demonstrates high performance for large scale problems. This session will introduce you to Artelys Knitro, its key features and modeling capabilities, with a particular emphasis on the latest major improvements including recent advances in solving mixed-integer nonlinear optimization problems. We will also provide benchmarks highlighting the power of Knitro to efficiently solve large-scale, nonlinear models with hundreds of thousands of variables and constraints.
Turning Models Into Applications– GAMS Engine and GAMS Transfer
Presented by: Adam Christensen & Steven Dirkse
IN-PERSON: Tuesday, October 26 11:45am-12:30pm PDT
VIRTUAL: Monday, October 25 7:45-8:30am PDT
The right tools help you deploy your GAMS model and maximize the impact of your decision support application. GAMS Engine is a powerful tool for solving GAMS models, either on-prem or in the cloud. Engine acts as a broker between applications or users with GAMS models to solve and the computational resources used for this task. Central to Engine is a modern REST API that provides an interface to a scalable Kubernetes-based system of services, providing API, database, queue, and a configurable number of GAMS workers. GAMS Transfer is an API (available in Python, Matlab, and soon R) that makes moving data between GAMS and your computational environment fast and easy. By leveraging open source data science tools such as Pandas/Numpy, GAMS Transfer is able to take advantage of a suite of useful (and platform independent) I/O tools to deposit data into GDX or withdraw GDX results to a number of data endpoints (i.e., visualizations, databases, etc.).
Multi-objective optimization and its Pareto extension
VIRTUAL: Monday, October 25 11:45am-12:30pm PDT
Presented by: Ferenc Katai (Product Manager- IBM CPLEX), Odellia Boni (Researcher), Evgeny Shindin (Researcher)
In real life optimization problems, we often seek solutions representing the best trade-offs between conflicting objectives. Existing methods dealing with multi-objective optimization usually output a solution representing a single pre-defined trade-off. In order to produce additional meaningful trade-offs, we present Diversity Maximization Algorithm (DMA) for Multi-objective optimization. This algorithm outputs a set of diverse optimal solutions that lie on Pareto Frontier, thus allowing the user to efficiently explore the optimal solutions space.
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Your Guide to Financial Portfolio Optimization with Excel/What’sBest!
IN-PERSON: Monday, October 25 2:45-3:30pm PDT
VIRTUAL: Sunday, October 24 6-6:45am PDT
Presented by: Linus Schrage, LINDO Systems
There has been an array of risk management optimization models proposed since Harry Markowitz first introduced the mean-variance model. Learn how easy it is to optimize with different risk metrics in Excel with the help of the What’sBest! add-in. In addition to mean-variance, we will cover:
- Semi-variance
- Mean Absolute Deviation (MAD)
- Sharpe Ratio
- Omega Ratio
- Sortino Ratio
- Information Ratio
- Value-at-Risk
- Conditional Value-at-Risk
- Power Utility Function
- Log Utility/Kelly criterion
- … and a variety of other benchmark tracking methods
By the end of the session, you will understand when each method should be applied, the common pitfalls of each approach, and the data preparation issues to be concerned with.
This IS IT! Interactive Smart Textbooks for the Modern Program!
IN-PERSON: Tuesday, October 26th 11-11:45am PDT
VIRTUAL: Monday, October 25th 8:30-9:15am PDT
Presented by: Jaret Wilson, Scotty Pectol
This is modern higher education! An affordable alternative to OER with up-to-date content from world-class author teams. Created by professors for professors, MyEducator smart interactive textbooks and learning resources are ideal for any classroom setting and work within live technology environments so your students don’t just learn, they do! Our approach enhances student engagement, improves learning outcomes, instructors receive better teaching evaluations, and students have more fun in the classroom.
Each smart learning resource is hosted on our intuitive platform with auto-graded assessments, ample instructor material, robust analytics, all with seamless single sign-on LMS integration, low student cost, lifetime access, and best-in-class service. Full access will be given to any book on our platform to attendees.
