causara is a Python package and GUI that enhances the entire lifecycle of a Gurobi model. We leverage advanced AI to create, adapt, and interact with Gurobi models.
Lifecycle of a Gurobi Model using Causara Features
01 Learning a Gurobi Model from Real-world / Synthetic Data
By gathering real-world historical data on optimized decisions—whether from domain experts or through experiments—we can train a Gurobi model to approximate this decision-making process. Additionally, synthetic data is often easy to generate for certain optimization problems.
Using such data, we can train a model that encodes the underlying optimization problem. This feature can be used to optimize problems that were previously impossible to encode in Gurobi.
Demos:
Create Better Gurobi Models
Main features of causara AI-toolkit:
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Learning a Gurobi Model from Real-World or Synthetic Data
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Compiling a plain Python function into a Gurobi Model
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Using Post-Processing Functions to better align with Real-World Objectives
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AI-Interface for end-users: easy no-code modification of Gurobi models in production by domain experts
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Fine-tuning a Gurobi model on real-world data to close the gap between model and reality
02 Compiling Python to Gurobi
Define your constraints and objectives using plain Python code—leveraging constructs like if, else, for loops, numpy functions, and assert statements. Our LLM-powered AI will automatically translate this Python code into a fully functional Gurobi model. This approach revolutionizes the process of building Gurobi models, eliminating complexity and making optimization more accessible than ever before. And this is just the beginning—we are developing methods to approximate any Python objective function as a Gurobi model using both Black-Box and Gray-Box techniques.
Demos:
03 Post-Processing Functions
Optimization problems are formulated as "models" to be solved by Gurobi. However, these models are always approximations of reality and rarely capture every real-world detail. To better align optimized decisions with real-world conditions, you can incorporate a custom post-processing Python function into your Gurobi model. This function can be any computation, including simulations, as long as it returns a single objective value.
Demos:
04 Intelligent Gurobi-Interface for End-Users
In many situations, temporary constraints are necessary. For example, in a TSP delivery problem, some stops must be visited early in the route due to priority requirements. However, implementing these adjustments requires programming skills, which end-users often lack. To address this, we introduce an intelligent GUI for Gurobi models, significantly simplifying their use for non-technical staff. This interface enables easy selection of Gurobi models from the cloud, starting and interrupting the solving process, modifying models using natural language commands, and more.
For instance, when faced with a priority requirement users can adjust the model with a simple command like "Ensure that city X and city Y are among the first 10 stops of the route".
The interface makes the entire optimization workflow more accessible and user-friendly. Please have a look at our Tutorial
05 Fine-tuning a Gurobi Model on Real-World Data
Optimization models often fall short of accurately representing real-world objectives and constraints as they often are developed in a controlled environment, with limited input from domain experts who ultimately use these models in production. In feature 5, the goal is to fine-tune the Gurobi model using real-world data collected in production. This refinement ensures that the model aligns more closely with real-world conditions, leading to more accurate and reliable predictions.
Demos:
ABOUT US
We are a team of optimization and machine learning experts aiming to revolutionize the way optimization problems are formulated and solved. Our vision is to build the world's most advanced AI system for mathematical optimization to enhance model accuracy, accessibility, and real-world impact.
WHY CAUSARA?
Create Better Gurobi Models
Effortlessly build, refine, and deploy Gurobi models with AI-driven automation and intelligent optimization.
01
Easier
Define your optimization problem directly in plain Python, making model development easier than ever. Non-technical users can seamlessly adjust Gurobi models in real-time using natural language.
Example: in a classic TSP problem, real-world constraints may require temporary modifications. With a simple command like, "ensure that city X and city Y are within the first 10 stops of the route", end-users can adjust the model without writing a single line of code.
02
Better
Learn a Gurobi model from historical or synthetic data to optimize problems that were previously impossible to encode in Gurobi. Fine-tune your models with real-world data and leverage AI-driven optimization to bridge the gap between theory and real-world decision-making. This ensures your models stay aligned with human preferences and evolving objectives. Refer to the documentation for example use cases.
03
Real-World Focused
Every feature of causara is designed to better align Gurobi models with real-world objectives. Traditionally, many Gurobi models are developed in a controlled environment with minimal feedback from end users. Once deployed, these models often remain unchanged and unoptimized. Causara challenges this outdated approach by enabling continuous improvement and real-world adaptability in Gurobi models.
