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Strict second-order positivity on the critical cone gives a strict local minimum
Statement
For the program of Inequality constrained programs, tangent cones, constraint qualifications, and KKT, suppose is feasible and has KKT multipliers . If
then is a strict local minimizer. The condition is vacuous when the critical cone is ; no constraint qualification is assumed.
Facts & Assumptions
Given: The program, feasible KKT tuple , and the strict positivity condition in the Statement.
A critical direction is in the linearized cone and satisfies on every positively weighted active inequality (Critical directions are tangent to every positively weighted active inequality).
The unit sphere in is compact by Heine–Borel, and continuous real functions attain their extrema on nonempty compact metric spaces (Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line, A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value).
A compact metric space has the finite-intersection property: nested nonempty closed subsets have nonempty intersection (A metric space is compact if and only if every family of closed subsets with the finite intersection property has nonempty intersection).
A scalar function has the second-order Taylor expansion with Peano remainder (Second-order Taylor expansion ).
Proof
Write and . The critical cone intersected with is closed in by its linear equalities and inequalities. If this intersection is nonempty, it is compact by [F2], and [F2] gives a minimum there. The relatively open set contains that intersection. If the intersection is empty, put .
For , let be the set of unit directions for feasible with and . If is empty for some , there is no such point in that neighborhood and strict local minimality holds. Otherwise, if bad points occur arbitrarily close with directions outside , the nonempty nested closed sets have the finite-intersection property in the compact sphere . By [F3] choose ; since is relatively open, and .
For each , the closure property in step 1.2 supplies a direction with from a feasible bad point , where and . Taylor expansion [F4] and equality feasibility imply ; active inequality feasibility gives for every active . The objective inequality gives . Letting grow in these bounds, using and , shows , on the active set, and . KKT stationarity and [F1] also give , so is a unit critical direction. This contradicts : if the critical unit set is empty there is no such , and otherwise every such belongs to . Hence for some , every feasible bad point with has direction in .
If the critical unit set is empty, step 2.1 already excludes all bad points sufficiently near , proving strict local minimality. Otherwise, let be as in step 1.1. At a feasible , KKT gives , , and . For bad points sufficiently close, step 2.1 gives , so . The Taylor expansion [F4] of the Lagrangian has a remainder , uniformly as ; after shrinking the neighborhood it yields . This contradicts . Therefore no distinct feasible point sufficiently near has objective at most , proving that is a strict local minimizer.
Depends on
- Inequality constrained programs, tangent cones, constraint qualifications, and KKT
- Critical directions are tangent to every positively weighted active inequality
- Second-order Taylor expansion $f(a+h)=f(a)+\nabla f(a)\cdot h+\tfrac12h^TH_f(a)h+o(\|h\|^2)$
- Heine-Borel in $\mathbb{R}^n$: with the Euclidean metric a subset of $\mathbb{R}^n$ is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line
- A metric space is compact if and only if every family of closed subsets with the finite intersection property has nonempty intersection
- A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value
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Sources
- Nocedal and Wright, Numerical Optimization, second edition (standard reference, not scraped)
- Boyd and Vandenberghe, Convex Optimization (standard reference, not scraped)