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Documents authored by Tan, Yong Kiam

Document
DOI: 10.4230/LIPIcs.ITP.2025.34
Verification of the CVM Algorithm with a Functional Probabilistic Invariant

Authors: Emin Karayel, Seng Joe Watt, Derek Khu, Kuldeep S. Meel, and Yong Kiam Tan

Published in: LIPIcs, Volume 352, 16th International Conference on Interactive Theorem Proving (ITP 2025)

Abstract
Estimating the number of distinct elements in a data stream is a classic problem with numerous applications in computer science. We formalize a recent, remarkably simple, randomized algorithm for this problem due to Chakraborty, Vinodchandran, and Meel (called the CVM algorithm). Their algorithm deviated considerably from the state of the art, due to its avoidance of intricate derandomization techniques, while still maintaining a close-to-optimal logarithmic space complexity. Central to our formalization is a new proof technique based on functional probabilistic invariants, which allows us to derive concentration bounds using the Cramér-Chernoff method without relying on independence. This simplifies the formal analysis considerably compared to the original proof by Chakraborty et al. Moreover, our technique opens up the possible algorithm design space; we demonstrate this by introducing and verifying a new variant of the CVM algorithm that is both total and unbiased - neither of which is a property of the original algorithm. In this paper, we introduce the proof technique, describe its use in mechanizing both versions of the CVM algorithm in Isabelle/HOL, and present a supporting formalized library on negatively associated random variables used to verify the latter variant.
Cite as

Emin Karayel, Seng Joe Watt, Derek Khu, Kuldeep S. Meel, and Yong Kiam Tan. Verification of the CVM Algorithm with a Functional Probabilistic Invariant. In 16th International Conference on Interactive Theorem Proving (ITP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 352, pp. 34:1-34:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{karayel_et_al:LIPIcs.ITP.2025.34,
  author =	{Karayel, Emin and Watt, Seng Joe and Khu, Derek and Meel, Kuldeep S. and Tan, Yong Kiam},
  title =	{{Verification of the CVM Algorithm with a Functional Probabilistic Invariant}},
  booktitle =	{16th International Conference on Interactive Theorem Proving (ITP 2025)},
  pages =	{34:1--34:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-396-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{352},
  editor =	{Forster, Yannick and Keller, Chantal},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITP.2025.34},
  URN =		{urn:nbn:de:0030-drops-246327},
  doi =		{10.4230/LIPIcs.ITP.2025.34},
  annote =	{Keywords: Verification, Isabelle/HOL, Randomized Algorithms, Distinct Elements}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.21
Practically Feasible Proof Logging for Pseudo-Boolean Optimization

Authors: Wietze Koops, Daniel Le Berre, Magnus O. Myreen, Jakob Nordström, Andy Oertel, Yong Kiam Tan, and Marc Vinyals

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Certifying solvers have long been standard for decision problems in Boolean satisfiability (SAT), allowing for proof logging and checking with very limited overhead, but developing similar tools for combinatorial optimization has remained a challenge. A recent promising approach covering a wide range of solving paradigms is pseudo-Boolean proof logging, but this has mostly consisted of proof-of-concept works far from delivering the performance required for real-world deployment. In this work, we present an efficient toolchain based on VeriPB and CakePB for formally verified pseudo-Boolean optimization. We implement proof logging for the full range of techniques in the state-of-the-art solvers RoundingSat and Sat4j, including core-guided search and linear programming integration with Farkas certificates and cut generation. Our experimental evaluation shows that proof logging and checking performance in this much more expressive paradigm is now quite close to the level of SAT solving, and hence is clearly practically feasible.
Cite as

Wietze Koops, Daniel Le Berre, Magnus O. Myreen, Jakob Nordström, Andy Oertel, Yong Kiam Tan, and Marc Vinyals. Practically Feasible Proof Logging for Pseudo-Boolean Optimization. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 21:1-21:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koops_et_al:LIPIcs.CP.2025.21,
  author =	{Koops, Wietze and Le Berre, Daniel and Myreen, Magnus O. and Nordstr\"{o}m, Jakob and Oertel, Andy and Tan, Yong Kiam and Vinyals, Marc},
  title =	{{Practically Feasible Proof Logging for Pseudo-Boolean Optimization}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{21:1--21:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.21},
  URN =		{urn:nbn:de:0030-drops-238825},
  doi =		{10.4230/LIPIcs.CP.2025.21},
  annote =	{Keywords: proof logging, certifying algorithms, combinatorial optimization, certification, pseudo-Boolean solving, 0-1 integer linear programming}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.8
Certifying Projected Knowledge Compilation

Authors: Randal E. Bryant, Yong Kiam Tan, and Marijn J. H. Heule

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
Knowledge compilers convert Boolean formulas, given in conjunctive normal form (CNF), into representations that enable efficient evaluation of unweighted and weighted model counts, as well as a variety of other useful properties. With projected knowledge compilation, the generated representation describes the restriction of the formula to a designated set of data variables, with the remaining ones eliminated by existential quantification. Projected knowledge compilation has applications in a variety of domains, including formal verification and synthesis. This paper describes a formally verified proof framework for certifying the output of a projected knowledge compiler. It builds on an earlier clausal proof framework for certifying the output of a standard knowledge compiler. Extending the framework to projected compilation requires a method to represent Skolem assignments, describing how the quantified variables can be assigned, given an assignment for the data variables. We do so by extending the representation generated by the knowledge compiler to also encode Skolem assignments. We also refine the earlier framework, moving beyond purely clausal proofs to enable scaling certification to larger formulas. We present experimental results obtained by making small modifications to the D4 projected knowledge compiler and extensions of our earlier proof generator. We detail a soundness argument stating that a compiler output that passes our certifier is logically equivalent to the quantified input formula; the soundness argument has been formally validated using the HOL4 proof assistant. The checker also ensures that the compiler output satisfies the properties required for efficient unweighted and weighted model counting. We have developed two proof checkers for the certification framework: one written in C and designed for high performance and one written in CakeML and formally verified in HOL4.
Cite as

Randal E. Bryant, Yong Kiam Tan, and Marijn J. H. Heule. Certifying Projected Knowledge Compilation. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 8:1-8:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bryant_et_al:LIPIcs.SAT.2025.8,
  author =	{Bryant, Randal E. and Tan, Yong Kiam and Heule, Marijn J. H.},
  title =	{{Certifying Projected Knowledge Compilation}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{8:1--8:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.8},
  URN =		{urn:nbn:de:0030-drops-237422},
  doi =		{10.4230/LIPIcs.SAT.2025.8},
  annote =	{Keywords: Knowledge Compilation, Propositional model counting, Proof checking}
}
Document
DOI: 10.4230/LIPIcs.SAT.2025.32
Efficient Certified Reasoning for Binarized Neural Networks

Authors: Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
Neural networks have emerged as essential components in safety-critical applications - these use cases demand complex, yet trustworthy computations. Binarized Neural Networks (BNNs) are a type of neural network where each neuron is constrained to a Boolean value; they are particularly well-suited for safety-critical tasks because they retain much of the computational capacities of full-scale (floating-point or quantized) deep neural networks, but remain compatible with satisfiability solvers for qualitative verification and with model counters for quantitative reasoning. However, existing methods for BNN analysis suffer from either limited scalability or susceptibility to soundness errors, which hinders their applicability in real-world scenarios. In this work, we present a scalable and trustworthy approach for both qualitative and quantitative verification of BNNs. Our approach introduces a native representation of BNN constraints in a custom-designed solver for qualitative reasoning, and in an approximate model counter for quantitative reasoning. We further develop specialized proof generation and checking pipelines with native support for BNN constraint reasoning, ensuring trustworthiness for all of our verification results. Empirical evaluations on a BNN robustness verification benchmark suite demonstrate that our certified solving approach achieves a 9× speedup over prior certified CNF and PB-based approaches, and our certified counting approach achieves a 218× speedup over the existing CNF-based baseline. In terms of coverage, our pipeline produces fully certified results for 99% and 86% of the qualitative and quantitative reasoning queries on BNNs, respectively. This is in sharp contrast to the best existing baselines which can fully certify only 62% and 4% of the queries, respectively.
Cite as

Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel. Efficient Certified Reasoning for Binarized Neural Networks. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 32:1-32:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{yang_et_al:LIPIcs.SAT.2025.32,
  author =	{Yang, Jiong and Tan, Yong Kiam and Soos, Mate and Myreen, Magnus O. and Meel, Kuldeep S.},
  title =	{{Efficient Certified Reasoning for Binarized Neural Networks}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{32:1--32:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.32},
  URN =		{urn:nbn:de:0030-drops-237665},
  doi =		{10.4230/LIPIcs.SAT.2025.32},
  annote =	{Keywords: Neural network verification, proof certification, SAT solving, approximate model counting}
}
Document
Artifact
DOI: 10.4230/DARTS.8.2.10
Verified Compilation and Optimization of Floating-Point Programs in CakeML (Artifact)

Authors: Heiko Becker, Robert Rabe, Eva Darulova, Magnus O. Myreen, Zachary Tatlock, Ramana Kumar, Yong Kiam Tan, and Anthony Fox

Published in: DARTS, Volume 8, Issue 2, Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Verified compilers such as CompCert and CakeML have become increasingly realistic over the last few years, but their support for floating-point arithmetic has thus far been limited. In particular, they lack the "fast-math-style" optimizations that unverified mainstream compilers perform. Supporting such optimizations in the setting of verified compilers is challenging because these optimizations, for the most part, do not preserve the IEEE-754 floating-point semantics. However, IEEE-754 floating-point numbers are finite approximations of the real numbers, and we argue that any compiler correctness result for fast-math optimizations should appeal to a real-valued semantics rather than the rigid IEEE-754 floating-point numbers. This document describes the artifact for RealCake, an extension of CakeML that achieves end-to-end correctness results for fast-math-style optimized compilation of floating-point arithmetic. This result is achieved by giving CakeML a flexible floating-point semantics and integrating an external proof-producing accuracy analysis. RealCake’s end-to-end theorems relate the I/O behavior of the original source program under real-number semantics to the observable I/O behavior of the compiler generated and fast-math-optimized machine code.
Cite as

Heiko Becker, Robert Rabe, Eva Darulova, Magnus O. Myreen, Zachary Tatlock, Ramana Kumar, Yong Kiam Tan, and Anthony Fox. Verified Compilation and Optimization of Floating-Point Programs in CakeML (Artifact). In Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022). Dagstuhl Artifacts Series (DARTS), Volume 8, Issue 2, pp. 10:1-10:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{becker_et_al:DARTS.8.2.10,
  author =	{Becker, Heiko and Rabe, Robert and Darulova, Eva and Myreen, Magnus O. and Tatlock, Zachary and Kumar, Ramana and Tan, Yong Kiam and Fox, Anthony},
  title =	{{Verified Compilation and Optimization of Floating-Point Programs in CakeML (Artifact)}},
  pages =	{10:1--10:2},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2022},
  volume =	{8},
  number =	{2},
  editor =	{Becker, Heiko and Rabe, Robert and Darulova, Eva and Myreen, Magnus O. and Tatlock, Zachary and Kumar, Ramana and Tan, Yong Kiam and Fox, Anthony},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.8.2.10},
  URN =		{urn:nbn:de:0030-drops-162086},
  doi =		{10.4230/DARTS.8.2.10},
  annote =	{Keywords: compiler verification, compiler optimization, floating-point arithmetic}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2022.1
Verified Compilation and Optimization of Floating-Point Programs in CakeML

Authors: Heiko Becker, Robert Rabe, Eva Darulova, Magnus O. Myreen, Zachary Tatlock, Ramana Kumar, Yong Kiam Tan, and Anthony Fox

Published in: LIPIcs, Volume 222, 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Verified compilers such as CompCert and CakeML have become increasingly realistic over the last few years, but their support for floating-point arithmetic has thus far been limited. In particular, they lack the "fast-math-style" optimizations that unverified mainstream compilers perform. Supporting such optimizations in the setting of verified compilers is challenging because these optimizations, for the most part, do not preserve the IEEE-754 floating-point semantics. However, IEEE-754 floating-point numbers are finite approximations of the real numbers, and we argue that any compiler correctness result for fast-math optimizations should appeal to a real-valued semantics rather than the rigid IEEE-754 floating-point numbers. This paper presents RealCake, an extension of CakeML that achieves end-to-end correctness results for fast-math-style optimized compilation of floating-point arithmetic. This result is achieved by giving CakeML a flexible floating-point semantics and integrating an external proof-producing accuracy analysis. RealCake’s end-to-end theorems relate the I/O behavior of the original source program under real-number semantics to the observable I/O behavior of the compiler generated and fast-math-optimized machine code.
Cite as

Heiko Becker, Robert Rabe, Eva Darulova, Magnus O. Myreen, Zachary Tatlock, Ramana Kumar, Yong Kiam Tan, and Anthony Fox. Verified Compilation and Optimization of Floating-Point Programs in CakeML. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 1:1-1:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{becker_et_al:LIPIcs.ECOOP.2022.1,
  author =	{Becker, Heiko and Rabe, Robert and Darulova, Eva and Myreen, Magnus O. and Tatlock, Zachary and Kumar, Ramana and Tan, Yong Kiam and Fox, Anthony},
  title =	{{Verified Compilation and Optimization of Floating-Point Programs in CakeML}},
  booktitle =	{36th European Conference on Object-Oriented Programming (ECOOP 2022)},
  pages =	{1:1--1:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-225-9},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{222},
  editor =	{Ali, Karim and Vitek, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2022.1},
  URN =		{urn:nbn:de:0030-drops-162290},
  doi =		{10.4230/LIPIcs.ECOOP.2022.1},
  annote =	{Keywords: compiler verification, compiler optimization, floating-point arithmetic}
}
Document
DOI: 10.4230/LIPIcs.ITP.2021.14
A Verified Decision Procedure for Univariate Real Arithmetic with the BKR Algorithm

Authors: Katherine Cordwell, Yong Kiam Tan, and André Platzer

Published in: LIPIcs, Volume 193, 12th International Conference on Interactive Theorem Proving (ITP 2021)

Abstract
We formalize the univariate fragment of Ben-Or, Kozen, and Reif’s (BKR) decision procedure for first-order real arithmetic in Isabelle/HOL. BKR’s algorithm has good potential for parallelism and was designed to be used in practice. Its key insight is a clever recursive procedure that computes the set of all consistent sign assignments for an input set of univariate polynomials while carefully managing intermediate steps to avoid exponential blowup from naively enumerating all possible sign assignments (this insight is fundamental for both the univariate case and the general case). Our proof combines ideas from BKR and a follow-up work by Renegar that are well-suited for formalization. The resulting proof outline allows us to build substantially on Isabelle/HOL’s libraries for algebra, analysis, and matrices. Our main extensions to existing libraries are also detailed.
Cite as

Katherine Cordwell, Yong Kiam Tan, and André Platzer. A Verified Decision Procedure for Univariate Real Arithmetic with the BKR Algorithm. In 12th International Conference on Interactive Theorem Proving (ITP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 193, pp. 14:1-14:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{cordwell_et_al:LIPIcs.ITP.2021.14,
  author =	{Cordwell, Katherine and Tan, Yong Kiam and Platzer, Andr\'{e}},
  title =	{{A Verified Decision Procedure for Univariate Real Arithmetic with the BKR Algorithm}},
  booktitle =	{12th International Conference on Interactive Theorem Proving (ITP 2021)},
  pages =	{14:1--14:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-188-7},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{193},
  editor =	{Cohen, Liron and Kaliszyk, Cezary},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITP.2021.14},
  URN =		{urn:nbn:de:0030-drops-139099},
  doi =		{10.4230/LIPIcs.ITP.2021.14},
  annote =	{Keywords: quantifier elimination, matrix, theorem proving, real arithmetic}
}
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