The semantics of decision problems are always essentially independent of the
underlying representation. Thus the space of input data (under appropriate
indexing) is closed
under action of the symmetrical group $S_n$ (for a specific data-size)
and the input-output relation is closed under the action of $S_n$.
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We develop and study the complexity of propositional proof systems of varying strength extending resolution by allowing it to operate with disjunctions of linear equations instead of clauses. We demonstrate polynomial-size refutations for hard tautologies like the pigeonhole principle, Tseitin graph tautologies and the clique-coloring tautologies in these proof systems. ... more >>>
We introduce the `binary value principle' which is a simple subset-sum instance expressing that a natural number written in binary cannot be negative, relating it to central problems in proof and algebraic complexity. We prove conditional superpolynomial lower bounds on the Ideal Proof System (IPS) refutation size of this instance, ... more >>>
We prove super-polynomial lower bounds on the size of propositional proof systems operating with constant-depth algebraic circuits over fields of zero characteristic. Specifically, we show that the subset-sum variant $\sum_{i,j,k,l\in[n]} z_{ijkl}x_ix_jx_kx_l-\beta = 0$, for Boolean variables, does not have polynomial-size IPS refutations where the refutations are multilinear and written as ... more >>>
The (extended) Binary Value Principle (eBVP, the equation $\sum x_i 2^{i-1} = -k$ for $k > 0$
and in the presence of $x_i^2=x_i$) has received a lot of attention recently, several lower
bounds have been proved for it [Alekseev et al 20, Alekseev 21, Part and Tzameret 21].
Also ...
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Strong algebraic proof systems such as IPS (Ideal Proof System; Grochow-Pitassi JACM 2018) offer a general model for
deriving polynomials in an ideal and refuting unsatisfiable propositional formulas, subsuming most standard propositional proof systems. A major approach for lower bounding the size of IPS refutations is the Functional Lower Bound ...
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