Unverified50% confidenceFactExact time
Gradient methods can usually only find a local optimum of the relaxed problem, and the rounded solution is not necessarily the optimal 0/1 solution.
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50%
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Long-term
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10/17/2026
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UnverifiedThis parameterization reduces the entire estimation problem to a convex optimization problem, and convexity means there is no trouble from local optima, and the solution process is stable and reproducible.78% similarUnverifiedVariational inequalities are more difficult to handle than standard convex optimization, mainly because the monotonicity of the operator is not equivalent to a gradient structure of some convex function.72% similarUnverifiedGradient descent naturally tends to select flatter, simpler solutions from among the many solutions that perfectly fit the training set, and this implicit regularization effect is closely related to generalization ability.72% similarUnverifiedEven if the relaxed problem attains the global optimum, there still exists an integrality gap between the rounded 0/1 solution and the true optimal solution, and its size depends on the specific instance structure.71% similarUnverifiedGradient estimation is valid only when the intervention magnitude is sufficiently small and the model is locally approximately linear; for large-scale steering or highly nonlinear response functions, the first-order approximation may be distorted.71% similar
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