{"id":783,"date":"2021-06-15T16:34:19","date_gmt":"2021-06-15T16:34:19","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/?page_id=783"},"modified":"2021-10-24T15:18:23","modified_gmt":"2021-10-24T15:18:23","slug":"technology-tutorials","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/technology-tutorials\/","title":{"rendered":"Technology Tutorials"},"content":{"rendered":"\n
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New Developments in AMPL:
Solver Callbacks, Spreadsheet Interfaces, and Cloud Licensing<\/strong><\/p>\n

IN-PERSON: Tuesday, October 25 2:45-3:30PM PDT
VIRTUAL: Sunday, October 24 8:30-9:15am PDT<\/p>\n

Presented by: Robert Fourer, AMPL Optimization Inc.<\/p>\n

Built on the concept of model-based optimization, AMPL\u2019s intuitive algebraic modeling language and prototyping environment give you a fast start on prescriptive decision-making projects. AMPL\u2019s APIs for popular programming languages then help you build completed optimization models into your applications. Now AMPL\u2019s 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\u2019s 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.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Nonlinear Optimization Using Artelys Knitro<\/h2>\n

IN-PERSON: Monday, October 25 11-11:45am PDT
VIRTUAL: Sunday, October 24 6:45-7:30am PDT<\/p>\n

Presented by: Richard Waltz, Senior Scientist, Artelys Corp, richard.waltz@artelys.com<\/a><\/p>\n

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.  <\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Turning Models Into Applications\u2013 GAMS Engine and GAMS Transfer<\/h2>\n

Presented by: Adam Christensen & Steven Dirkse
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IN-PERSON: Tuesday, October 26 11:45am-12:30pm PDT
VIRTUAL: Monday, October 25 7:45-8:30am PDT<\/span><\/p>\n

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.).<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Multi-objective optimization and its Pareto extension<\/h2>\n

VIRTUAL: Monday, October 25 11:45am-12:30pm PDT<\/span><\/p>\n

Presented by: Ferenc Katai (Product Manager- IBM CPLEX), Odellia Boni (Researcher), Evgeny Shindin (Researcher)<\/p>\n

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.<\/p>\n

When you interact with IBM, this serves as your authorization to INFORMS or its vendor to provide your contact information to IBM in order for IBM to follow up on your interaction. IBM’s use of your contact information is governed by the IBM Privacy <\/em>Policy<\/em><\/a> <\/em><\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Your Guide to Financial Portfolio Optimization with Excel\/What\u2019sBest!<\/em><\/span><\/h2>\n

IN-PERSON:<\/span> <\/span>Monday, October 25 2:45-3:30pm PDT
VIRTUAL: Sunday, October 24 6-6:45am PDT<\/span><\/p>\n

Presented by: Linus Schrage, LINDO Systems<\/p>\n

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\u2019sBest!<\/em> add-in. In addition to mean-variance, we will cover:<\/p>\n