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A full SQP step is rejected by the L1 merit test and repaired by SOC
Statement
Let and . At a feasible point , use multiplier estimate and SQP Hessian model . For nonzero sufficiently small , the SQP step has predicted decrease , but its full step increases the L1 merit for every . The minimum-norm SOC correction makes the full corrected step pass the Armijo test with for every fixed and all sufficiently small nonzero .
Facts & Assumptions
Given: The equality-constrained SQP and L1 merit definitions, the SOC formula, and , .
The linearized quadratic subproblem in sequential quadratic programming imposes in the subproblem.
L1 exact-penalty merit and predicted decrease for SQP defines the merit and predicted decrease .
Second-order correction reduces the active constraint residual to cubic order gives the local quadratic correction size and cubic active-residual order under its smooth full-rank hypotheses.
L1 merit globalization with finite penalty and backtracking budgets tests the full SOC candidate against the Armijo threshold.
is sufficiently small and nonzero, is fixed, , the multiplier estimate is , and .
Proof
At , , , and . The unit tangent satisfies . The QP constraint gives ; minimizing on this line gives . Its QP multiplier is , since .
Here , so [F3] gives and . Direct substitution into the quadratic constraint yields The corrected first coordinate is , so the corrected objective change is . Adding the penalty contribution gives
Since and , . Also , so [F2] gives . The objective changes by , while the equality violation changes from zero to ; hence the full-step merit change is , which fails the Armijo right side .
Divide the corrected merit change plus by . With , the result is , which tends to as . It is therefore negative for all sufficiently small nonzero , proving the SOC Armijo test passes. The explicit correction norm and residual satisfy the quadratic-size and cubic-residual bounds of [F4].
Depends on
- The linearized quadratic subproblem in sequential quadratic programming
- L1 exact-penalty merit and predicted decrease for SQP
- L1 merit globalization with finite penalty and backtracking budgets
- Minimum-norm second-order correction for identified active constraints
- Second-order correction reduces the active constraint residual to cubic order
Used by
Nothing in the library uses this result yet.
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Sources
- Jorge Nocedal and Stephen J. Wright, Numerical Optimization, 2nd ed. (standard reference, not scraped)