--- license: mit task_categories: - tabular-classification - tabular-regression language: - en tags: - blockchain - np-hard - computational-complexity - proof-of-useful-work - cryptography - optimization - sat - tsp - subset-sum pretty_name: COINjecture NP Solutions v5 size_categories: - 10K # 🧮 COINjecture NP Solutions v5 ### The Empirical Evolution **Live solutions to NP-hard computational problems from the COINjecture blockchain** [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Dataset](https://img.shields.io/badge/🤗-Dataset-blue)](https://huggingface.co/datasets/COINjecture/v5) [![Network](https://img.shields.io/badge/Network-Live-brightgreen)](https://coinjecture.com) [Documentation](https://coinjecture.com/docs) • [GitHub](https://github.com/Quigles1337/GITHUB-SUCKS) • [Website](https://coinjecture.com) --- ## 📋 Overview This dataset contains **real-time solutions to NP-hard problems** mined from the COINjecture Network B blockchain. Unlike traditional Proof-of-Work that wastes computational energy, COINjecture implements **Proof-of-Useful-Work (PoUW)** — every hash contributes to solving computationally significant problems. ### What Makes v5 Special | Feature | v4 (Previous) | v5 (Current) | |---------|---------------|--------------| | **Tokenomics** | Hardcoded constants | 100% empirical (network-derived) | | **Light Clients** | Basic SPV | FlyClient + MMR proofs | | **Node Classification** | Single type | 6 specialized types | | **Mobile Support** | None | WASM + C-FFI SDK | | **Metrics** | Static | Live network oracle | --- ## 🎯 Problem Types ### Subset Sum Given a set of integers, find a subset that sums to a target value. - **Complexity**: NP-complete - **Applications**: Cryptography, resource allocation, financial modeling ### Boolean Satisfiability (SAT) Determine if a Boolean formula can be satisfied. - **Complexity**: NP-complete (Cook-Levin theorem) - **Applications**: Hardware verification, AI planning, scheduling ### Traveling Salesman Problem (TSP) Find the shortest route visiting all cities exactly once. - **Complexity**: NP-hard - **Applications**: Logistics, circuit design, DNA sequencing ### Custom Problems User-submitted computational challenges with bounties. - **Complexity**: Variable (verified NP-hard) - **Applications**: Research, optimization, real-world problems --- ## 📊 Dataset Schema ```json { "problem_id": "uuid-v4", "problem_type": "SubsetSum | SAT3 | TSP | Custom", "problem_data": { "elements": [1, 2, 3, ...], "target": 42 }, "solution_data": { "selected_indices": [0, 2, 5], "selected_elements": [1, 3, 10] }, "block_height": 12345, "timestamp": 1733500000, "block_hash": "0x1a2b3c...", "prev_block_hash": "0x9f8e7d...", "work_score": 100.0, "solution_quality": 1.0, "problem_complexity": 3.5, "bounty": "1000000", "solve_time_us": 150000, "verify_time_us": 1200, "energy_ratio": 1920.5, "solver": "12D3KooW...", "submitter": "12D3KooW...", "network_metrics": { "hash_rate": 1.5, "peer_count": 25, "consensus_agreement": 0.95 } } ``` ### Field Descriptions | Field | Type | Description | |-------|------|-------------| | `problem_id` | string | Unique identifier for the problem | | `problem_type` | enum | Category of NP-hard problem | | `problem_data` | object | Problem-specific input data | | `solution_data` | object | Verified solution | | `block_height` | integer | Block number in the chain | | `timestamp` | integer | Unix timestamp of block creation | | `work_score` | float | Computational work performed | | `solution_quality` | float | Optimality measure (1.0 = optimal) | | `problem_complexity` | float | Estimated problem difficulty | | `bounty` | string | Reward in microCOIN (u128 as string) | | `solve_time_us` | integer | Solution time in microseconds | | `verify_time_us` | integer | Verification time in microseconds | | `energy_ratio` | float | Verification/solve energy ratio | --- ## 🚀 Quick Start ### Load with Hugging Face Datasets ```python from datasets import load_dataset # Load the full dataset dataset = load_dataset("COINjecture/v5") # Iterate through solutions for record in dataset["train"]: print(f"Block {record['block_height']}: {record['problem_type']}") print(f" Work Score: {record['work_score']}") print(f" Energy Ratio: {record.get('energy_ratio', 'N/A')}x") ``` ### Filter by Problem Type ```python # Get only SAT problems sat_problems = dataset["train"].filter( lambda x: x["problem_type"] == "SAT3" ) # Get only high-complexity problems hard_problems = dataset["train"].filter( lambda x: x["problem_complexity"] > 4.0 ) ``` ### Stream Large Datasets ```python # Stream without downloading entire dataset dataset = load_dataset("COINjecture/v5", streaming=True) for record in dataset["train"]: process(record) ``` --- ## 📈 Statistics | Metric | Value | |--------|-------| | **Update Frequency** | Real-time (every block) | | **Avg Block Time** | ~30 seconds | | **Problem Types** | 4 | | **Verification Rate** | 100% | | **Energy Asymmetry** | >1000x (v5 with ADZDB) | --- ## 🔬 Research Applications ### Machine Learning - Train models to predict problem difficulty - Learn heuristics for NP-hard optimization - Benchmark solver algorithms ### Cryptography - Study hash function distributions - Analyze computational hardness assumptions - Research post-quantum implications ### Distributed Systems - Study consensus mechanisms - Analyze network behavior under load - Research incentive-compatible protocols --- ## 🧮 Empirical Tokenomics (v5) v5 introduces **zero hardcoded constants**. All economic parameters are derived from live network state: ``` ┌─────────────────────────────────────────────────────────────────┐ │ NETWORK METRICS ORACLE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ Hash Rate ──→ Emission Bounds │ │ Solve Times ──→ Problem Hardness Factor │ │ Median Fees ──→ Base Storage Cost │ │ Stake Dist ──→ Staking Thresholds │ │ Fault Impact ──→ Reputation Severities │ │ │ │ Formula: value = f(network_state) │ │ Result: Self-regulating, governance-free economics │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` --- ## 📱 LightSync Protocol v5 implements **FlyClient** with Merkle Mountain Ranges for O(log n) chain verification: | Protocol | Bandwidth | Use Case | |----------|-----------|----------| | Full Sync | O(n) | Archive nodes | | SPV | O(n) headers | Desktop wallets | | **FlyClient** | **O(log n)** | **Mobile devices** | For 1M blocks: - SPV: ~80 MB of headers - FlyClient: ~50 KB (proofs + sampled headers) --- ## 🔗 Related Resources | Resource | Link | |----------|------| | **v4 Dataset** | [COINjecture/NP_Solutions_v4](https://huggingface.co/datasets/COINjecture/NP_Solutions_v4) | | **v3 Dataset** | [COINjecture/NP_Solutions_v3](https://huggingface.co/datasets/COINjecture/NP_Solutions_v3) | | **GitHub** | [Quigles1337/GITHUB-SUCKS](https://github.com/Quigles1337/GITHUB-SUCKS) | | **Website** | [coinjecture.com](https://coinjecture.com) | --- ## 📜 Citation ```bibtex @dataset{coinjecture_v5_2025, title = {COINjecture NP Solutions Dataset v5}, author = {COINjecture Network Contributors}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/COINjecture/v5}, note = {Real-time NP-hard problem solutions from Proof-of-Useful-Work blockchain} } ``` --- ## ⚖️ License This dataset is released under the **MIT License**. You are free to use, modify, and distribute the data for any purpose, including commercial applications. ---
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