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1. An Examination of Linear Combinations of Skew-Adjoint Type Algebraic Curvature Tensors

   Brundan, J (March 2021, PUMP journal of undergraduate research)

   Dunn, C; Raianu, S (Ed.)

   In this paper a new potential invariant of algebraic curvature tensors, the sig- nature, will be examined. Furthermore, linear combinations of skew-adjoint type algebraic curvature tensors will be thoroughly examined so as to provide some insight into the possible forms of algebraic curvature tensors. 

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2. Concise tensors of minimal border rank

   https://doi.org/10.1007/s00208-023-02569-y  

   Jelisiejew, Joachim; Landsberg, J. M.; Pal, Arpan (April 2023, Mathematische Annalen)

   We determine defining equations for the set of concise tensors of minimal border rank in Cm⊗Cm⊗Cm when m = 5 and the set of concise minimal border rank 1∗-generic tensors when m = 5, 6. We solve the classical problem in algebraic complexity theory of classifying minimal border rank tensors in the special case m = 5. Our proofs utilize two recent developments: the 111-equations defined by Buczy´nska–Buczy´nski and results of Jelisiejew–Šivic on the variety of commuting matrices. We introduce a new algebraic invariant of a concise tensor, its 111-algebra, and exploit it to give a strengthening of Friedland’s normal form for 1-degenerate tensors satisfying Strassen’s equations. We use the 111-algebra to characterize wild minimal border rank tensors and classify them in C5⊗C5⊗C5. 

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3. Efficient Computation of a Semi-Algebraic Basis of the First Homology Group of a Semi-Algebraic Set

   https://doi.org/10.1007/s00454-024-00626-0  

   Basu, Saugata; Percival, Sarah (September 2024, Discrete & Computational Geometry)

   Let $$\R$$ be a real closed field and $$\C$$ the algebraic closure of $$\R$$. We give an algorithm for computing a semi-algebraic basis for the first homology group, $$\HH_1(S,\mathbb{F})$$, with coefficients in a field $$\FF$$, of any given semi-algebraic set $$S \subset \R^k$$ defined by a closed formula. The complexity of the algorithm is bounded singly exponentially. More precisely, if the given quantifier-free formula involves $$s$$ polynomials whose degrees are bounded by $$d$$, the complexity of the algorithm is bounded by $$(s d)^{k^{O(1)}}$$. This algorithm generalizes well known algorithms having singly exponential complexity for computing a semi-algebraic basis of the zero-th homology group of semi-algebraic sets, which is equivalent to the problem of computing a set of points meeting every semi-algebraically connected component of the given semi-algebraic set at a unique point. It is not known how to compute such a basis for the higher homology groups with singly exponential complexity. As an intermediate step in our algorithm we construct a semi-algebraic subset $$\Gamma$$ of the given semi-algebraic set $$S$$, such that $$\HH_q(S,\Gamma) = 0$$ for $q=0,1$. We relate this construction to a basic theorem in complex algebraic geometry stating that for any affine variety $$X$$ of dimension $$n$$, there exists Zariski closed subsets \[ Z^{(n-1)} \supset \cdots \supset Z^{(1)} \supset Z^{(0)} \] with $$\dim_\C Z^{(i)} \leq i$, and $$\HH_q(X,Z^{(i)}) = 0$$ for $$0 \leq q \leq i$$. We conjecture a quantitative version of this result in the semi-algebraic category, with $$X$$ and $$Z^{(i)}$$ replaced by closed semi-algebraic sets. We make initial progress on this conjecture by proving the existence of $$Z^{(0)}$$ and $$Z^{(1)}$$ with complexity bounded singly exponentially (previously, such an algorithm was known only for constructing $$Z^{(0)}$$). 

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4. The hexatope and octatope abstract domains for neural network verification

   https://doi.org/10.1007/s10703-024-00457-y  

   Bak, Stanley; Dohmen, Taylor; Subramani, K; Trivedi, Ashutosh; Velasquez, Alvaro; Wojciechowski, Piotr (December 2024, Formal Methods in System Design)

   Efficient verification algorithms for neural networks often depend on various abstract domains such as intervals, zonotopes, and linear star sets. The choice of the abstract domain presents an expressiveness vs. scalability trade-off: simpler domains are less precise but yield faster algorithms. This paper investigates the hexatope and octatope abstract domains in the context of neural net verification. Hexatopes are affine transformations of higher-dimensional hexagons, defined by difference constraint systems, and octatopes are affine transformations of higher-dimensional octagons, defined by unit-two-variable-per-inequality constraint systems. These domains generalize the idea of zonotopes which can be viewed as affine transformations of hypercubes. On the other hand, they can be considered as a restriction of linear star sets, which are affine transformations of arbitrary H-Polytopes. This distinction places hexatopes and octatopes firmly between zonotopes and linear star sets in their expressive power, but what about the efficiency of decision procedures? An important analysis problem for neural networks is the exact range computation problem that asks to compute the exact set of possible outputs given a set of possible inputs. For this, three computational procedures are needed: (1) optimization of a linear cost function; (2) affine mapping; and (3) over-approximating the intersection with a half-space. While zonotopes allow an efficient solution for these approaches, star sets solves these procedures via linear programming. We show that these operations are faster for hexatopes and octatopes than they are for the more expressive linear star sets by reducing the linear optimization problem over these domains to the minimum cost network flow, which can be solved in strongly polynomial time using the Out-of-Kilter algorithm. Evaluating exact range computation on several ACAS Xu neural network benchmarks, we find that hexatopes and octatopes show promise as a practical abstract domain for neural network verification. 

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5. Leibniz International Proceedings in Informatics (LIPIcs):38th Computational Complexity Conference (CCC 2023)

   https://doi.org/10.4230/LIPIcs.CCC.2023.4  

   Galesi, Nicola; Grochow, Joshua A.; Pitassi, Toniann; She, Adrian (January 2023, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Ta-Shma, Amnon (Ed.)

   The Tensor Isomorphism problem (TI) has recently emerged as having connections to multiple areas of research within complexity and beyond, but the current best upper bound is essentially the brute force algorithm. Being an algebraic problem, TI (or rather, proving that two tensors are non-isomorphic) lends itself very naturally to algebraic and semi-algebraic proof systems, such as the Polynomial Calculus (PC) and Sum of Squares (SoS). For its combinatorial cousin Graph Isomorphism, essentially optimal lower bounds are known for approaches based on PC and SoS (Berkholz & Grohe, SODA '17). Our main results are an Ω(n) lower bound on PC degree or SoS degree for Tensor Isomorphism, and a nontrivial upper bound for testing isomorphism of tensors of bounded rank. We also show that PC cannot perform basic linear algebra in sub-linear degree, such as comparing the rank of two matrices (which is essentially the same as 2-TI), or deriving BA=I from AB=I. As linear algebra is a key tool for understanding tensors, we introduce a strictly stronger proof system, PC-Inv, which allows as derivation rules all substitution instances of the implication AB=I → BA=I. We conjecture that even PC-Inv cannot solve TI in polynomial time either, but leave open getting lower bounds on PC-Inv for any system of equations, let alone those for TI. We also highlight many other open questions about proof complexity approaches to TI. 

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