Timeline for Convex-Constrained Nonconvex-Nonconcave Minimax Problem
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 25, 2021 at 2:30 | comment | added | Keith | As @worldsmithhelper said, I want to obtain a solution that has a certain property. | |
| Oct 24, 2021 at 10:08 | comment | added | worldsmithhelper | I understood "globally convergent" as guaranteeing to hit a point with certain properties (such as first or second order optimality criteria) from every (or no) starting point. | |
| Oct 23, 2021 at 14:01 | comment | added | Mark L. Stone | The term "globally convergent" is frequently misunderstood. it means the algorithm converges to something, no matter the starting point. It does not means it converges to a global optimum. | |
| Oct 23, 2021 at 10:16 | comment | added | worldsmithhelper | If you don't care about evaluating $\max _{y\in Y} f(x,y)$ correctly, you can just use a local NLP solver instead but you will be solving a very different problem, | |
| Oct 23, 2021 at 8:52 | vote | accept | Keith | ||
| Oct 23, 2021 at 8:52 | comment | added | Keith | I appreciate your detailed answer to this question. I do not know much about global optimization so it helped me to understand the practical way to solve the minimax problem. Unfortunately, the problem I consider is that the dimension of y is much high, that's why it is enough to obtain the solution that does not necessarily converge to the global optimal point but local optimal point for the inner maximization problem. | |
| Oct 22, 2021 at 17:30 | history | edited | worldsmithhelper | CC BY-SA 4.0 |
added 623 characters in body
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| Oct 22, 2021 at 17:04 | history | answered | worldsmithhelper | CC BY-SA 4.0 |