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Log-Driven Semantic Tuning for Mixed Integer Programming Solvers

Automated configuration of mixed-integer programming (MIP) solvers largely relies on black-box optimization. Each trial requires an expensive solver run and provides only a scalar outcome, so tuning can require many such evaluations. Human experts take a different approach. They inspect solver logs, identify performance bottlenecks, and adjust the parameters that address them. We propose Log-Drive

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Developers and AI engineers can take advantage of new developer credits and free token allowances for AI Platform and related services announced in Log-Driven Semantic Tuning for Mixed Integer Programming Solvers.

Offer Details & Included Quota - **Verified Provider**: AI Platform - **Developer Access**: Direct API endpoint access, free token quotas, and playground evaluation. - **Target Workflows**: Prototyping, serverless inference, agentic orchestration, and benchmarking.

Step-by-Step Claim Instructions 1. Navigate to the primary redemption link at [AI Platform Access Portal](https://arxiv.org/abs/2611.00621v1). 2. Create or link your developer account (GitHub or email authentication). 3. Access your API dashboard to generate keys and activate your promotional credit balance.

Technical Implementation Snippet

python
from openai import OpenAI

# Initialize client using OpenAI-compatible standard
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.aiplatform.com/v1"
)

response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Test inference connectivity"}]
)
print(response.choices[0].message.content)
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