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Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching

ICML 2023 · Optimization › Nonconvex and smooth optimization · approach: Adaptive first-order convex methods · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use AdaSketch-Newton with Gaussian-vector sketches instead of the deterministic inexact SQP baselinesConstrained optimization · CUTEst (47 problems)total flopsHelped
Use AdaSketch-Newton with randomized Kaczmarz sketches instead of the deterministic inexact SQP baselinesConstrained optimization · CUTEst (47 problems)total flopsHelped
Use AdaSketch-Newton with Gaussian-vector sketches instead of the deterministic inexact SQP baselinesConstrained optimization · CUTEst (47 problems)KKT residualHelped
Use AdaSketch-Newton with randomized Kaczmarz sketches instead of the deterministic inexact SQP baselinesConstrained optimization · CUTEst (47 problems)objective and constraints evaluationsHelped
Use an adaptive deterministic solver accuracy instead of a fixed accuracy boundDeterministic inexact SQP · CUTEst (47 problems)overall criteriaHelped
Use AdaSketch-Newton with Gaussian-vector sketches instead of the deterministic inexact SQP baselinesConstrained optimization · Constrained logistic regression (7 LIBSVM datasets)KKT residualHelped
Use AdaSketch-Newton instead of the deterministic inexact SQP baselinesConstrained optimization · Constrained logistic regression (7 LIBSVM datasets)total flopsHelped
Use AdaSketch-Newton instead of deterministic SQP and standard augmented LagrangianConstrained optimization · PDE-constrained optimal controloverall criteriaHelped
Vary the initial penalty parameters, accuracy threshold, and line-search parameter (ablation)AdaSketch-Newton-GV · CUTEst (47 problems)overall criteriaNo clear effect
Let the sketching solver accuracy factor decrease toward zero (ablation)AdaSketch-Newton · Local convergenceconvergence rateHelped

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