MMP.8:11 - SoTA-Echoing

Boyd and Vandenberghe, Convex Optimization, section 4.1, distinguish the feasible set from the objective and transformations of a problem. Adopt that separation in :4.1 and :4.3. The present finite constructions do not require convexity; the book’s convex solution guarantees apply only under their conditions.

Ben-Tal, Goryashko, Guslitzer and Nemirovski, Adjustable robust solutions of uncertain linear programs (2004), develop adjustable decisions alongside choices fixed before uncertainty is revealed. Adapt this distinction into the information-dependent formulation; do not transfer linear-program tractability to unrestricted policies.

Duchi, Optimization with uncertain data (2018), sections 1 and 6, compares uncertainty-set requirements with probabilistic ones and makes choosing the uncertainty set a modeling question. Adopt that choice explicitly; no worst-case objective is imposed by this pattern.

Vayanos, Georghiou and Yu, Robust Optimization with Decision-Dependent Information Discovery, version 3 (2022), treats actions that affect when uncertainty can be observed. Carry that extension into :4.2 and the return to method design. Its specialized algorithms are further methods, not assumed capabilities of every reader.

The interval, allocation and bit-response examples are constructed demonstrations of the method under their stated assumptions.