Add power cone benchmarks and iteration experiment script#28
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Add power cone benchmarks and iteration experiment script#28
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Add comprehensive benchmarks comparing: - power() atom with various exponents (convex p=1.5, negative p=-0.5, fractional p=0.3) - geo_mean() atom with uniform weights, large scale (n=500), and weighted configurations - pnorm() atom with various p values (p=1.5, p=3, p=0.5) Each benchmark tests both: - approx=True: Uses SOC approximation via rational decomposition - approx=False: Uses native power cones Problem sizes: - power: n=5000, n=1000 - geo_mean: n=100 (quick), n=500 (large/stress test) - pnorm: n=5000, n=1000 - weighted geo_mean: n=100 Tests are run with both CLARABEL and SCS solvers. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Adds a standalone script to compare iteration counts and solve times between power cones (approx=False) and SOC approximation (approx=True) for geo_mean, pnorm, power, and inv_prod atoms. Supports CLARABEL, SCS, and MOSEK solvers. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Summary
approx=True(SOC approximation) vsapprox=False(native power cones)power_cone_iterations.py) for detailed iteration count experimentsBenchmarks added (
power_cone_benchmarks.py)power()with n=5000, n=1000geo_mean()with n=100, n=500pnorm()with n=5000, n=1000geo_meanwith n=100Iteration experiment script (
power_cone_iterations.py)Standalone script to compare iteration counts, time per iteration, and cone breakdown:
Reports variables, constraints, cone breakdown, iterations, solve time, and time/iteration.
Test plan
cc @rileyjmurray - Ready for review
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