How do you validate your optimization assumptions?

2 Test your assumptions

The second step to validate your optimization assumptions is to test them using data and methods that are relevant and appropriate for your problem. You should collect or generate data that reflects the real-world conditions and scenarios that your AI system will face, and use statistical or analytical tools to check if your assumptions hold or not. For example, you can use hypothesis testing, correlation analysis, sensitivity analysis, or simulation to test if your objective function is linear, if your constraints are feasible, or if your data is normally distributed.

3 Adjust your assumptions

The third step to validate your optimization assumptions is to adjust them based on the results of your tests. You should compare your expected and observed outcomes, and identify any discrepancies or errors that may indicate that your assumptions are invalid or inaccurate. You should then modify or refine your assumptions to better match the reality, or change your optimization method or model to suit your new assumptions. For example, you can use nonlinear, nonconvex, or stochastic optimization techniques if your objective function is not linear, convex, or deterministic.

4 Evaluate your optimization

The fourth step to validate your optimization assumptions is to evaluate your optimization performance and quality using metrics and criteria that are meaningful and relevant for your problem. You should measure how well your optimization solution or configuration meets your objectives and constraints, and how robust and reliable it is in different situations and environments. You should also compare your optimization results with other alternatives or benchmarks, and assess the trade-offs and limitations of your approach. For example, you can use accuracy, efficiency, scalability, or resilience as metrics to evaluate your optimization.

5 Communicate your assumptions

The fifth step to validate your optimization assumptions is to communicate them to your stakeholders and users, and explain how they affect your optimization process and outcome. You should provide clear and transparent documentation and visualization of your assumptions, and justify why they are reasonable and necessary for your problem. You should also acknowledge any uncertainties or risks that may arise from your assumptions, and how you mitigate or handle them. For example, you can use graphs, tables, or charts to show your assumptions, and use scenarios, examples, or stories to illustrate their impact.

6 Update your assumptions

The sixth step to validate your optimization assumptions is to update them regularly and continuously, as your problem, data, or environment may change over time. You should monitor and review your optimization performance and quality, and collect feedback and evidence from your stakeholders and users. You should also check if there are any new or emerging factors or trends that may affect your optimization problem or objective, and adapt your assumptions accordingly. For example, you can use data analysis, surveys, or interviews to update your assumptions, and use experiments, trials, or pilots to verify them.