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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2510.16055 |
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| _version_ | 1866912656968908800 |
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| author | Zadeh, Norman |
| author_facet | Zadeh, Norman |
| contents | In 2011, Friedmann [F 7] claimed to have proved that pathological linear programs existed for which the Simplex method using Zadeh's least-entered rule [Z 14] would take an exponential number of pivots. In 2019, Disser and Hopp [DH 5] argued that there were errors in Friedmann's 2011 construction. In 2020, Disser, Friedmann, and Hopp [DFH 3,4] again contended that the least-entered rule was exponential. We show that their arguments contain multiple flaws. In other words, the worst-case behavior of the least-entered rule has not been established. Neither [F 7] nor [DFH 3,4] provides pathological linear programs that can be tested. Instead, the authors contend that their pathological linear programs are of the form (P) as shown on page 12 of [DFH 3]. The authors contend that the constraints of (P) ensure that the probability of entering a vertex u is equal to the probability of exiting u. In fact, we note that the authors' constraints (P) are flawed in at least three ways: a) they require the probability of exiting u to exceed the probability of entering u, b) they require the probability of exiting some nodes to exceed 1, and c) they overlook flows from decision nodes to decision nodes. At my request, in August of 2025, Disser, Friedmann, and Hopp provided me with their first ten purportedly pathological LPs and the graph of their first purportedly pathological Markov Decision Process (MDP1). It is shown that: a) their first two pathological LPs are infeasible if the variables are supposed to be probabilities, as the authors contend, and b) their first purportedly pathological LP does not match up with their first purportedly pathological MDP. In other words, the authors have not come close to providing counterexamples to the least-entered rule. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_16055 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Is Zadeh's Least-Entered Pivot Rule Exponential? Zadeh, Norman Data Structures and Algorithms Optimization and Control In 2011, Friedmann [F 7] claimed to have proved that pathological linear programs existed for which the Simplex method using Zadeh's least-entered rule [Z 14] would take an exponential number of pivots. In 2019, Disser and Hopp [DH 5] argued that there were errors in Friedmann's 2011 construction. In 2020, Disser, Friedmann, and Hopp [DFH 3,4] again contended that the least-entered rule was exponential. We show that their arguments contain multiple flaws. In other words, the worst-case behavior of the least-entered rule has not been established. Neither [F 7] nor [DFH 3,4] provides pathological linear programs that can be tested. Instead, the authors contend that their pathological linear programs are of the form (P) as shown on page 12 of [DFH 3]. The authors contend that the constraints of (P) ensure that the probability of entering a vertex u is equal to the probability of exiting u. In fact, we note that the authors' constraints (P) are flawed in at least three ways: a) they require the probability of exiting u to exceed the probability of entering u, b) they require the probability of exiting some nodes to exceed 1, and c) they overlook flows from decision nodes to decision nodes. At my request, in August of 2025, Disser, Friedmann, and Hopp provided me with their first ten purportedly pathological LPs and the graph of their first purportedly pathological Markov Decision Process (MDP1). It is shown that: a) their first two pathological LPs are infeasible if the variables are supposed to be probabilities, as the authors contend, and b) their first purportedly pathological LP does not match up with their first purportedly pathological MDP. In other words, the authors have not come close to providing counterexamples to the least-entered rule. |
| title | Is Zadeh's Least-Entered Pivot Rule Exponential? |
| topic | Data Structures and Algorithms Optimization and Control |
| url | https://arxiv.org/abs/2510.16055 |