Tootfinder

Indifference-Zone Relaxation Procedures for Finding Feasible Systems
Yuwei Zhou, Sigr\'un Andrad\'ottir, Seong-Hee Kim, Chuljin Park
https://arxiv.org/abs/2509.04514 htt…

Indifference-Zone Relaxation Procedures for Finding Feasible Systems
We consider the problem of finding feasible systems with respect to stochastic constraints when system performance is evaluated through simulation. Our objective is to solve this problem with high computational efficiency and statistical validity. Existing indifference-zone (IZ) procedures introduce a fixed tolerance level, which denotes how much deviation the decision-maker is willing to accept from the threshold in the constraint. These procedures are developed under the assumption that all s…

Testing the variety hypothesis
A. Lerario, P. Roos Hoefgeest, M. Scolamiero, A. Tamai
https://arxiv.org/abs/2507.16705 https://arxiv.…

Testing the variety hypothesis
Given a probability measure on the unit disk, we study the problem of deciding whether, for some threshold probability, this measure is supported near a real algebraic variety of given dimension and bounded degree. We call this "testing the variety hypothesis". We prove an upper bound on the so-called "sample complexity" of this problem and show how it can be reduced to a semialgebraic decision problem. This is done by studying in a quantitative way the Hausdorff geometry of the space of real a…

Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
Reza Hadavian, Dmitri Truhachev
https://arxiv.org/abs/2506.20079

Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
We propose a reduced complexity approach to pattern-based soft decoding of block codes. We start from the ORDEPT decoding algorithm which tests a list of partial error patterns organized in the order of their likelihood and attempts to complete the patterns creating candidate codewords. We then propose an early termination criterion. Once a candidate codeword is found, its log-likelihood difference to the received sequence is compared to a preset threshold and the decoding decision is instantly…

Structural Abstraction and Refinement for Probabilistic Programs
Guanyan Li, Juanen Li, Zhilei Han, Peixin Wang, Hongfei Fu, Fei He
https://arxiv.org/abs/2508.12344 https://

Structural Abstraction and Refinement for Probabilistic Programs
In this paper, we present structural abstraction refinement, a novel framework for verifying the threshold problem of probabilistic programs. Our approach represents the structure of a Probabilistic Control-Flow Automaton (PCFA) as a Markov Decision Process (MDP) by abstracting away statement semantics. The maximum reachability of the MDP naturally provides a proper upper bound of the violation probability, termed the structural upper bound. This introduces a fresh ``structural'' characterizati…