InsideOpt-Seeker GPT-AI-driven optimization solver.
AI-powered optimization made simple.
Help me build my first Seeker model!
How do I install InsideOpt Seeker?
What sets Seeker apart from other optimization solvers?
How does Seeker help me manage the risk that comes with decision-making under uncertainty?
Related Tools
Load More
GPT Finder
Discover the best Custom GPT at OpenAI's GPT Finder
GPT Finder 👉🏼 Best +68.000 GPT Search
Search all public GPTs in one place. Find the best Custom versions of ChatGPT tailored to your needs. Every day, hundreds of new popular AI tools join our ranking. Discover the best alternatives in the store with this tool finder.
GPT Finder 🔍
GPT Finder 🔍 is a search tool designed for efficiently finding the best custom GPTs from a selection of over 133,000 GPTs. It initially checks a specialized database and then utilizes Google search if necessary, ensuring that users find the best match amo
GPT Finder
Find best GPTs for your specific purposes, intelligently browse the web of 20000+ GPTs
GPT Finder
Findet neue GPTs zu deinen Wunschthemen
Best GPT Finder 👉🏼 89527 GPT Search
Discover the perfect GPTs tailored just for you from an astounding selection of 89527 models! Dive in and enjoy the magic! The GPT repository will update continuously!
20.0 / 5 (200 votes)
InsideOpt-Seeker GPT Overview
InsideOpt-Seeker GPT is a specialized tool designed to assist developers in navigating the InsideOpt-Seeker optimization solver. It provides precise, detailed guidance on building, tuning, and managing optimization models using Seeker's unique stochastic and deterministic optimization features. The GPT acts as an intelligent assistant, capable of answering highly specific technical questions about Seeker's capabilities, helping users to define decision variables, constraints, and objectives, and to leverage Seeker’s superior performance in scenarios that require managing uncertainty, multiple objectives, and non-linear optimizations. For instance, Seeker excels in managing nested optimizations and stochastic problems with its powerful parallelization capabilities and automatic tuning functions. These functions enable it to outperform traditional solvers, providing faster solutions without the need for deep expertise in operations research.
Key Functions of InsideOpt-Seeker
Env Creation
ExampleEnv(string license)
ScenarioThe function is used to create an optimization environment with a valid Seeker license. This is the foundation of any optimization process, where users define whether the environment will support stochastic models or not, making it applicable for risk analysis or deterministic scenarios.
Stochastic Aggregation
ExampleEnv::continuous_uniform(double low, double high)
ScenarioThis function returns a uniformly distributed random value, essential for modeling uncertain outcomes. For example, a financial institution may use this to simulate uncertain asset returns within a specific range in a portfolio optimization task.
Nested Linear Optimization
ExampleLP lp(vector<Term> objTerms, vector<Term> varBounds, vector<Term> rowBounds, vector<vector<Term>> matrix, bool maximize)
ScenarioThis function allows solving complex optimization models involving nested linear programs. A supply chain management company could use this to optimize production scheduling, where multiple levels of decision-making are required, such as handling inventory and demand simultaneously.
Ideal Users for InsideOpt-Seeker GPT
Optimization Experts
Optimization experts will find Seeker invaluable due to its capacity to handle highly complex models such as multi-objective, stochastic, and nested optimizations. Seeker’s automatic tuning and parallelization features will enable experts to deliver high-performance solutions faster than with traditional solvers.
Business Analysts and Data Scientists
Business analysts and data scientists benefit from Seeker's strong integration with data workflows, allowing them to optimize decision-making models in sectors like finance, supply chain, and logistics without requiring deep knowledge in operations research. Seeker's ability to integrate with machine learning models makes it ideal for predictive analytics tasks.
How to Use InsideOpt-Seeker GPT
Step 1
Visit aichatonline.org for a free trial without login, also no need for ChatGPT Plus.
Step 2
Install Seeker by running `pip install insideopt-seeker` to enable access to optimization capabilities.
Step 3
Familiarize yourself with the documentation and user manuals to understand the available functions and optimization techniques offered by Seeker.
Step 4
Begin by defining decision variables, setting constraints, and optimizing objectives using Seeker’s environment class and its built-in functions.
Step 5
For more complex or stochastic problems, leverage Seeker’s AI-based search and stochastic optimization techniques for faster and better results.
Try other advanced and practical GPTs
Cari Cature
AI-Powered Caricatures for Fun and Art
CharmCraft
AI-powered communication for dating success.
Lean Six Sigma Guide
AI-powered Lean Six Sigma solutions.
Practical Internal Medicine
AI-Powered Clinical Resource for Doctors
OpenCVGPT
AI-powered tool for mastering OpenCV.
USMLE Step 2 Study Buddy
AI-powered study tool for USMLE Step 2
Cat GPT
AI-powered creativity with a feline twist.
Harvey Specter - Lawyer:USA (Search online) v3.0
AI-driven legal expertise at your fingertips.
Comedy Writer
AI-powered tool for crafting humor.
File Baby
Secure Your Digital Assets with AI.
Gsheet AI GPT: MixerBox ChatGSheet
AI-powered Google Sheets management
Coach Titre Pro DWWM
AI-powered simulator for web development certification.
- Risk Management
- Financial Modeling
- Supply Chain
- AI Search
- Stochastic Optimization
InsideOpt-Seeker GPT Q&A
What makes InsideOpt-Seeker unique?
InsideOpt-Seeker excels in managing complex optimization problems like stochastic and non-linear programming. It also supports nested optimizations and parallelized search, offering up to 100 times faster performance than traditional solvers.
How do I create decision variables in Seeker?
You can create decision variables by using the `Term` class, such as `Term continuous(l, h)` for continuous variables or `Term ordinal(l, h)` for integer-valued decision variables.
What types of optimization problems can InsideOpt-Seeker handle?
Seeker can handle linear, integer, non-linear, stochastic, and multi-objective optimization problems. It is ideal for scenarios with nested optimizations and uncertain data.
How does InsideOpt-Seeker integrate with machine learning?
Seeker allows you to integrate machine learning models into the optimization process, enabling data-driven decision-making and enhancing optimization performance.
Can I run Seeker across multiple processors?
Yes, Seeker supports massively parallel processing. You can run multiple solvers on different processors or even asynchronously coordinate them using Seeker’s built-in parallelization capabilities.