NP-Hard
A class of computational problems for which no known algorithm can find optimal solutions in polynomial time. As problem size grows, solve time grows exponentially.
Explanation
NP-hard problems are the hardest optimization challenges. Route planning, production scheduling, and bin packing are all NP-hard. This means that for large instances, finding the provably optimal solution may take impractical amounts of time. Practical approaches use exact methods with smart pruning (branch-and-bound), heuristics for fast good solutions, or hybrid approaches.
How kint Uses It
kint handles NP-hard problems by combining exact optimization methods with machine learning. ML models predict good starting solutions (warm starts), reducing the search space. kint reports the optimality gap so users know exactly how close to optimal their solution is.
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kint applies NP-Hard to solve real business problems. Let us show you what's possible.
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