how are we computing? invalidation certificates consider invalidating the constraints (prior info,...
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How are we computing? Invalidation Certificates
Consider invalidating the constraints (prior info, and N dataset units)
The invalidation certificate is a binary tree, with L leaves. At the i’th leaf – coordinate-aligned cube
– Polynomial/rational functions (“surrogate models”) & error bounds, which satisfy
– sum of squares certificate proving the emptiness of
Moreover Caveat: with each Mj relatively complex, these error bounds are generally heuristic,
implicitly assuming regularity in Mj
Building Quadratic, rational surrogates on
Given L samples of M, namely
Relaxing the denominator condition number bound leads to a quasi-convex problem, solved with bisection and semidefinite programming.
– Richer approximation than quadratics only
– Parametrization is only twice as large (as quadratics)
– Non-explicit control of behavior off sample points (with κ)
peak fitting error
using rational, quadratic function
on samples
with condition number bound on denominator over the domain (not just sample points)
minimize
Quadratic, rational surrogates on
Given L samples of M, namely
Enforce using the S-procedure:
Find nonnegative and such that
Quadratic forms… nonnegative everywhere
Two (1+n)x(1+n) matrices that depend affinely on coefficients of
D and λ and τ must be positive semidefinite
Quadratic, rational surrogates on
Given L samples of M, namely
But ensures that , so
Quadratic, rational surrogates on
Given L samples of M, namely
For fixed t– linear constraints on (coefficients of) D and N
– Semidefinite constraints on (coefficients of) D and λ and τ
– linear constraints on λ and τ
– Check feasibility with, e.g., SeDuMi
Bisect on t to optimal
Prediction with Rational surrogates
Given 1+P models and data
“Bound”: Inner and Outer bounds to minimum and maximum value that can take on
Local search
S-procedure, weak duality
Prediction with rational surrogates
The prediction problem is
But so
Minimize such that the S-procedure proves
Fixed , SDP. Bisect to optimal outer bound
So, in this context, many computations involve proving empty intersections of systems of quadratic inequalities.