Quick Start Guide
Get started with Sematryx in minutes. This guide will walk you through your first optimization problem.
1. Installation
Install Sematryx using pip:
pip install sematryx2. Get Your API Key
For API access, get your API key from the API Keys page. Choose a plan that fits your needs and complete the checkout process.
3. Your First Optimization
Define your objective function and bounds, then let Sematryx find the optimal solution:
from sematryx import Sematryx
# Initialize the client
client = Sematryx(api_key="sk-your-api-key")
# Define your objective function
def sphere(x):
return sum(xi**2 for xi in x)
# Run optimization
result = client.optimize(
objective="minimize",
variables=[
{"name": "x", "bounds": (-5, 5)},
{"name": "y", "bounds": (-5, 5)},
],
objective_function=sphere,
)
print(f"Solution: {result.solution}")
print(f"Value: {result.value}")What happened?
- Sematryx analyzed your problem and selected the best optimization strategy
- The optimizer evaluated your function to find the optimal solution
- The result includes the best parameters, objective value, and explanation
4. Configure the Optimization Engine
Sematryx's optimization engine is built on three pillars of intelligence. Configure them to match your needs:
from sematryx import Sematryx
client = Sematryx(api_key="sk-your-api-key")
# Use optimization modes
result = client.optimize(
objective="minimize",
variables=[{"name": "x", "bounds": (-5, 5)}, {"name": "y", "bounds": (-5, 5)}],
objective_function=sphere,
mode="balanced" # speed, balanced, quality
)
# Get explainable results
result = client.optimize(
objective="minimize",
variables=[{"name": "x", "bounds": (-5, 5)}, {"name": "y", "bounds": (-5, 5)}],
objective_function=sphere,
explanation_level=2 # 0=none, 1=basic, 2=detailed, 3=comprehensive
)
print(result.explanation)The Three Core Pillars
- 🤖 Agentic: Multi-agent coordination for strategy selection
- 📖 Interpretable: Explainable results with configurable detail levels
- 🧠Adaptive: Self-improvement through learning from experience
5. Domain-Specific Optimization
Use specialized optimization libraries for specific business domains:
from sematryx import Sematryx
client = Sematryx(api_key="sk-your-api-key")
# Portfolio optimization
result = client.optimize_portfolio(
assets=["AAPL", "GOOGL", "MSFT", "AMZN"],
returns=[0.12, 0.10, 0.08, 0.15],
covariance=[...], # 4x4 covariance matrix
target_return=0.10,
max_position=0.4,
explanation_level=2 # Get audit trail
)
print(f"Allocation: {result.solution}")
print(f"Sharpe Ratio: {result.metrics['sharpe_ratio']}")Available Domains
- Financial: Portfolio optimization, trading strategies
- Healthcare: Drug discovery, clinical trials
- Supply Chain: Vehicle routing, inventory management
- AI/ML: Hyperparameter tuning, architecture search
- Marketing: Campaign optimization, budget allocation
- Conversational: Natural language problem creation with AI agent
6. Key Concepts
Understanding Optimization
- Objective Function: The function you want to minimize or maximize
- Bounds: Search space constraints for each variable
- Strategy Selection: Sematryx automatically chooses the best optimization algorithm
- Explainability: Get detailed explanations of optimization decisions
- Learning: Sematryx improves performance on repeated problems
7. Next Steps
Explore Tutorials
Follow step-by-step tutorials to solve real-world optimization problems.
View Tutorials →API Reference
Detailed documentation for all optimization APIs and intelligence configuration.
API Reference →Intelligence Configuration
Learn how to configure Sematryx's 3 Core Pillars: Agentic, Interpretable, and Adaptive intelligence.
Configuration Guide →