Name: Build optimization models and turn them into full featured applications with Gurobi and Anaconda
Uploaded: 2015-12-17T16:08:09.000Z
Duration: 53 min 17 s
Description: Building optimization models and turning them into full featured applications with Gurobi and Anaconda

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### Predictive Analytics Techniques
User sentiment regarding Gurobi Optimizer's predictive analytics techniques capabilities is generally positive, with users highlighting its ability to efficiently handle diverse optimization problems such as linear programming, quadratic programming, mixed-integer linear programming, and mixed-integer programming. The platform's capacity to solve non-linear expressions and its effectiveness in solving scheduling problems for large logistic companies have been particularly commended. While some users have mentioned the need to convert non-linear problems into linear or quadratic formulations, overall, Gurobi Optimizer's predictive analytics techniques are seen as a valuable asset in optimizing complex models and delivering optimal solutions.

### Optimization Models
Reviewers consistently praise Gurobi Optimizer for its robust optimization models capabilities. Users highlight the platform's efficiency in solving diverse optimization problems, including linear programming, quadratic programming, mixed-integer linear programming, and mixed-integer programming. The ease of building models using the Python interface is also commended, with users recommending the Python API for its simplicity and flexibility. Despite some users noting the need to convert non-linear problems into linear or quadratic formulations, overall sentiment leans towards the effectiveness and versatility of Gurobi Optimizer's optimization models.

### Programming and APIs
Users consistently highlight the user-friendly nature of Gurobi Optimizer's programming and API features, particularly emphasizing the ease of integration with Python and MATLAB. The well-documented APIs and the availability of cloud solutions for testing complex models are also commended. While some users mention a need for more detailed documentation for the Python bindings, overall, the sentiment leans towards positive experiences with Gurobi Optimizer's programming and API functionalities.

### Support and Resources
The feedback from users regarding Gurobi Optimizer's support and resources is generally positive. Users appreciate the extensive resources, well-documented API, and responsive support staff. The availability of examples and documentation has been highlighted as beneficial for both new and experienced users. While some users have suggested improvements in the documentation and examples, overall, the support and resources provided by Gurobi Optimizer seem to be a valuable asset for users tackling optimization problems.

Users have found success in utilizing Gurobi to solve a range of linear and non-linear programming problems across various industries. Some notable use cases include supply chain management, where Gurobi has been instrumental in optimizing resource allocation and route optimization. The software has also been effective in the haul industry, helping users tackle complex optimization problems related to transportation and logistics. 

Gurobi's capabilities have extended beyond these fields as well. It has been widely employed in electrical power systems engineering to determine the most suitable generator for supplying electricity during peak consumption phases. Moreover, Gurobi has been invaluable in industries such as academia, airlines, and police departments for applications including system biology optimization problems and resource allocation.

Customers have praised Gurobi for its user-friendly Optimizer API for Python, which offers flexibility and ease of use, particularly for assortment optimization problems with linear and non-linear objective functions. Additionally, users have expressed appreciation for Gurobi's performance and ability to handle complicated dynamic programming problems swiftly and efficiently. Its compatibility with other platforms like Python and MATLAB has further enhanced its value proposition.

Overall, Gurobi has emerged as a reliable alternative solver that provides great support and reduces stress for users across a wide range of industries. It helps businesses make better decisions, improve performance, and achieve optimal outcomes in areas such as bid optimization, route optimization, and supply chain network problems within transportation and logistics.

Low Performance in Some Functions: Several reviewers have expressed dissatisfaction with the performance of certain functions in Gurobi Optimizer, specifically those related to the hint of optimal solutions. These users have experienced subpar performance and are not fully satisfied with this aspect of the software.

Inability to Solve Non-Linear Programs: Gurobi Optimizer is unable to solve non-linear programs, as mentioned by multiple reviewers. This limitation can be a drawback for users who require solving complex optimization problems that involve non-linearities.

Expensive Development Licensing: The licensing cost for development purposes is considered expensive by some users. They have suggested that improvements could be made to make it more affordable and accessible for developers.

Ease of Use: Users have found Gurobi Optimizer to be very easy to use, with many reviewers mentioning its user-friendly nature. They appreciate the convenience it offers in building models using the Python interface without having to learn a new language.

Optimization Capabilities: Many users praise Gurobi Optimizer for its strong optimization capabilities and ability to solve diverse optimization problems quickly and efficiently. Reviewers mention that it is suitable for real-time decision-making scenarios and can handle thousands of variables and tens of thousands of constraints in just a few minutes.

Integration with Python: The integration of Gurobi Optimizer with Python has been highly appreciated by users. They find it convenient as they can write code in Python without the need to learn a new language. This integration allows for seamless building and implementation of models using the well-documented API and functionality provided by Gurobi Optimizer.

Users of Gurobi have made several recommendations based on their experiences with the software. The most common recommendations include:

1. Use the Python API: Many users have found Gurobi to be a good package and recommend using the Python API to build mathematical models. This approach is seen as effective in solving big and complicated mathematical problems.

2. Refer to the Gurobi Optimizer documentation: Users suggest referring to the documentation provided by Gurobi for learning and practical application of the software. It is considered a valuable resource for understanding the capabilities and features of the optimizer.

3. Consider Gurobi for optimization needs: Several users strongly recommend switching to Gurobi for solving complex optimization problems. They praise its power, speed, and efficiency in handling mathematical optimization tasks. Additionally, users suggest considering the Gurobi cloud API for specific optimization needs that align with its service usage scenario.

Overall, users find Gurobi to be a useful tool for various applications, especially in academic research or industry settings where mathematical optimization plays a crucial role.

Gurobi Optimizer is a software solution designed to address complex business challenges by providing detailed action plans through mathematical modeling. According to the vendor, the software is suitable for organizations of all sizes, from small businesses to large enterprises. It is utilized by various professionals and industries including business analysts, operations research professionals, supply chain managers, financial planners, and manufacturing and production managers.

## Key Features

**Unmatched Performance**: According to the vendor, Gurobi Optimizer consistently outperforms competing solvers by delivering both feasible and proven optimal solutions. The software's powerful MIP algorithm allows for the efficient solution of complex models that accurately represent real-world scenarios within a reasonable time frame.

**Support for All Major Problem Types**: Gurobi Optimizer supports a wide range of problem types, including linear programming (LP), mixed-integer programming (MIP), quadratic programming (QP), and more. The software provides specialized algorithms and techniques for different problem types, aiming to ensure efficient and accurate solutions.

**Responsive, Expert Support**: The vendor claims that Gurobi offers responsive and expert support to assist users in effectively utilizing the software and solving optimization problems. The support team consists of optimization experts who provide guidance and assistance in formulating and solving models. Comprehensive documentation, tutorials, and best practices are also available to aid users.

**Flexible Licensing**: According to the vendor, Gurobi provides flexible licensing options to accommodate different user needs and usage scenarios. The software can be licensed for individual users, teams, or enterprise-wide deployments. Users can choose between perpetual licenses or annual subscriptions based on their preferences and budget.

**Web License Service**: Gurobi's Web License Service (WLS) allows users to dynamically deploy the software in various containerized environments, such as Docker and Kubernetes. The vendor states that WLS supports different deployment scenarios across Linux, macOS, and Windows, providing flexibility and scalability.

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Tutorial: Modeling with the Gurobi Python Interface

The Gurobi Python interface allows you to build concise and efficient optimization models using high-level modeling constructs. This 50 minute video tutorial provides an overview of these capabilities, including detailed examples that show how to use the Python interface to build models that can be turned into full optimization applications.

--
Learn more about Gurobi Optimization here:
https://www.gurobi.com/

Check out our Optimization Application Demos here:
https://www.gurobi.com/resources/?cat...

Check out our 2,400 customers:
https://www.gurobi.com/customers/exam...

--
About Gurobi

Gurobi produces the world’s fastest and most powerful mathematical optimization solver – the Gurobi Optimizer – which is used by leading global companies across more than 40 different industries to rapidly solve their complex, real-world problems and make automated decisions that optimize their efficiency and profitability.  

As the market leader in mathematical optimization software, we aim to deliver not only the best solver, but also the best support – so that companies can fully leverage the power of mathematical optimization (on its own or in combination with other AI techniques such as machine learning) to drive optimal business decisions and outcomes. 

Founded in 2008, Gurobi has operations across the USA, Europe, and Asia and more than 2,400 customers globally including Air France, Uber, and the National Football League (NFL). For more information, please visit https://www.gurobi.com/ or call +1 713 871 9341.

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Like Us: https://www.facebook.com/GurobiOptimi...
Follow Us: https://twitter.com/gurobi
Connect with Us: https://www.linkedin.com/company/guro...


#optimization #datascience #dataanalytics #machinelearning #analytics #gurobipy

Solving Simple Stochastic Optimization Problems with Gurobi

The importance of incorporating uncertainty into optimization problems has always been known; however, both the theory and software were not up to the challenge to provide meaningful models that could be solved within a reasonable run time.

Over the last 15 years, the continuous improvements made to the theoretical as well as the algorithmic area of stochastic and mixed integer linear optimization have changed this situation dramatically.

In this 35-minute video recording, we will focus on stochastic optimization models and easy-to-understand algorithms, amenable to being easily solved with Gurobi. The intended audience for this webinar includes those with a background in optimization and knowledge on basic probability and statistics.

This recording consists of:

- A quick introduction to stochastic optimization
- Types of stochastic optimization problems
- Types of models that can be solved easily: two-stage stochastic problems with expected value and coherent risk measures
- Overview of the main algorithms: sample average approximation
- Examples of common problems with Gurobi

Presenting this webinar is Dr. Daniel Espinoza, Senior Developer at Gurobi Optimization.

Dr. Espinoza holds a Ph.D. in Operations Research from Georgia Institute of Technology. He has published numerous papers in the fields of mathematical programming, computer optimization and operations research. Prior to joining Gurobi, he was Associate Professor in the Department of Industrial Engineering at the Universidad de Chile.

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Learn more about Gurobi Optimization here: https://www.gurobi.com/ 

Check out our Optimization Application Demos here: https://www.gurobi.com/resources/?category-filter=demos

Check out our 2,400 customers: https://www.gurobi.com/customers/example-customers/
-- 

Our Mission 
Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math programming solver, tools for distributed optimization, optimization in the cloud, and outstanding support. We are committed to improving our solver performance and developing tools to help you use Gurobi with more ease. 

Founded in 2008 by arguably the most experienced and respected team in optimization circles, we have successfully expanded to serving over 2,400 companies from a wide range of industries, by way of providing the right mix of advanced developments and technologies, world-class support and flexible licensing. 

-- 

Like Us: https://www.facebook.com/GurobiOptimization/

Follow Us: https://twitter.com/gurobi 

Connect with Us: https://www.linkedin.com/company/gurobi-optimization/

#optimization #datascience #dataanalytics #machinelearning #analytics #research #operationsresearch #Gurobi #gurobipy #AI #artificialintelligence #algorithms #mathematicaloptimization #jupyternotebook #heuristics #MIP #mixedintegerprogramming #MIQP #python

Building optimization models and turning them into full featured applications with Gurobi and Anaconda

Build optimization models and turn them into full featured applications with Gurobi and Anaconda

Developers of optimization and prescriptive analytics models often feel that they need to choose between the ease of a modeling language and the power of a programming language.

However, the high-level modeling constructs Gurobi has embedded in Python, deliver the ease of a modeling language inside a powerful programming language. Anaconda, a modern data analytics platform powered by Python, provides more than 300 of the most popular Python and R packages for data science. The combination offers a complete solution for data ingestion, model solution, and visualization. 

Based on our experience, since python is a very readable and easy programming language to get started with, we think you’ll be most productive with the Gurobi-Python environment – even if you are already familiar with another programming language. 

In this webinar, we will cover: why choose Gurobi as your solver, why choose Python as your programming language, why choose Anaconda as your analytics platform, how Gurobi and Python can be used together to build models and applications, and an overview of useful Python resources including: Spyder and Jupyter for development, Pandas to help you manage data and Bokeh to help you visualize your results.

--

Learn more about Gurobi Optimization here: https://www.gurobi.com/ 

Check out our Optimization Application Demos here: https://www.gurobi.com/resources/?category-filter=demos

Check out our 2,400 customers: https://www.gurobi.com/customers/example-customers/
-- 

Our Mission 
Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math programming solver, tools for distributed optimization, optimization in the cloud, and outstanding support. We are committed to improving our solver performance and developing tools to help you use Gurobi with more ease. 

Founded in 2008 by arguably the most experienced and respected team in optimization circles, we have successfully expanded to serving over 2,400 companies from a wide range of industries, by way of providing the right mix of advanced developments and technologies, world-class support and flexible licensing. 

-- 

Like Us: https://www.facebook.com/GurobiOptimization/

Follow Us: https://twitter.com/gurobi 

Connect with Us: https://www.linkedin.com/company/gurobi-optimization/

#optimization #datascience #dataanalytics #machinelearning #analytics #research #operationsresearch #Gurobi #gurobipy #AI #artificialintelligence #algorithms #mathematicaloptimization #jupyternotebook #heuristics #MIP #mixedintegerprogramming #MIQP #python

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