Black-box & Simulation-based
Cost from a simulator, digital twin, or ERP — no formula, no gradients.
In plain terms
Sometimes the cost can’t be written as a formula at all — you only have a program that, given a choice, runs a simulation and tells you how good it was. Quicopt can optimize against such a “black box” directly: it tries decisions, learns from the answers, and homes in on the best one.
The technical picture
In many real problems the cost of a decision is not a formula but the output of a simulator, a digital twin, or an ERP model. There are no gradients to differentiate and no branch-and-bound proof tree to build.
Quicopt works from input–output evaluations alone: you give it a function that maps a decision to a cost, and it searches for the best decision — treating your model as a black box.
Mathematical model▾
Minimize an objective that is only available as an oracle: evaluated by simulation, with no closed form.
Example
This problem class is coming to the API soon. Today you can run LP, QP, MILP, MINLP, QUBO, PUBO and NLP models — and if this class is the one that matters to you, tell us and we’ll keep you posted.
Benchmark
How Quicopt performs on representative black-box objectives.
Measured results for this class are being prepared and will appear here.