SIROM
Robust optimization without having to pick an uncertainty budget first.
A sampling-based method that hands you a Pareto frontier of robust solutions and lets you choose the trade-off after seeing the options, instead of committing to an uncertainty budget before you know what it costs.
| Measure | Value |
|---|---|
| Bandwidth Packing cases matched or beaten | 92.5% |
| Pipeline runtime, after optimization | 30.034s → 1.027s (29.2×) |
| Frontier envelope reproduced across constraint structures | 18 / 18 |
The problem with asking first
Plenty of optimization problems have coefficients nobody knows exactly. Demand, cost, capacity — they live in a range rather than at a point. Classical robust optimization handles this by making you specify, up front, how much uncertainty you want protection against. That is the uncertainty budget, and it is a strange thing to ask of a decision-maker: choose your insurance premium before anyone has told you what the policy covers.
The honest answer is usually "it depends what it costs" — and you cannot know what it costs until you have solved the problem.
What SIROM does instead
SIROM takes a linear program whose coefficients are intervals and inverts the question. It samples the uncertain region, solves each realization, clusters the resulting solutions by how they behave, and returns a small Pareto frontier of candidates — each labelled with its objective value and the probability it stays feasible under uncertainty.
You pick the trade-off afterwards, looking at real numbers.
The method was published in Expert Systems with Applications in 2022, co-authored with Michel Gendreau, and evaluated on the Bandwidth Packing Problem, where it matched or outperformed the methods in the literature in 92.5% of cases.
What the frontier actually shows you
Each point is a candidate plan. Moving right buys you a higher probability of staying feasible; moving up costs you objective value. The interesting part is that the frontier is rarely smooth — it has cliffs, and the cliffs are where the real decisions live.
Push the target return high enough and the frontier does not just shift, it collapses: the set of achievable robust solutions shrinks, sampled futures start coming back infeasible, and the method tells you plainly that what you asked for is not available at any robustness worth having.
Engineering notes
The interesting work was not the maths. Getting the pipeline from 30.034 seconds to 1.027 seconds — a 29.2× speed-up — required per-phase attribution rather than guessing, and the profile was counter-intuitive: the clustering step cost more than every linear program in the sweep combined.
That result generalises. When a pipeline mixes a "real" solver with ordinary data manipulation, intuition consistently blames the solver.
A note on fidelity
A browser port of this method cannot reproduce the Python output bit-for-bit — k-means initialisation is randomised, and no two implementations will cluster identically. The defensible claim, which the repository's own benchmarks support across 18 of 18 constraint structures, is that the frontier's envelope is invariant: the range of objective values and feasibility probabilities matches, even when the specific set of points does not.
That distinction is worth stating out loud rather than hiding. A demo that claims more than it can prove is worse than no demo.