{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T08:32:53Z","timestamp":1767429173529,"version":"3.48.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Algorithms"],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    <jats:sc>Max-SAT<\/jats:sc>\n                    with cardinality constraint (\n                    <jats:sc>CC-Max-Sat<\/jats:sc>\n                    ) is one of the classical NP-complete problems, that generalizes\n                    <jats:sc>Maximum Coverage<\/jats:sc>\n                    ,\n                    <jats:sc>Partial Vertex Cover<\/jats:sc>\n                    ,\n                    <jats:sc>Max-2-SAT<\/jats:sc>\n                    with bisection constraints, and has been extensively studied across all algorithmic paradigms. In this problem, we are given a CNF formula\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\Phi\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , and a positive integer\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , and the goal is to find an assignment\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\beta\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    with at most\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    variables set to true (also called a\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -weight assignment) such that the number of clauses satisfied by\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\beta\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    is maximized. The problem is known to admit an approximation algorithm with factor\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(1-\\frac{1}{e}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , which is probably optimal. Furthermore, assuming Gap-Exponential Time Hypothesis (Gap-ETH), for any\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\epsilon &gt; 0\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and any function\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( h \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , no\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(h(k)(n+m)^{o(k)}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    time algorithm can approximate\n                    <jats:sc>Maximum Coverage<\/jats:sc>\n                    (a monotone version of\n                    <jats:sc>CC-Max-Sat<\/jats:sc>\n                    ) with\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( n \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    elements and\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( m \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    sets to within a factor\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\((1-\\frac{1}{e}+\\epsilon)\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , even with a promise that there exist\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    sets that fully cover the whole universe. In fact, the problem is hard to approximate within 0.929, assuming Unique Games Conjecture, even when the input formula is 2-CNF. These intractable results lead us to explore families of formula, where we can circumvent these barriers. Toward this, we consider\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(K_{d,d}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -free formulas (that is, the clause-variable incidence bipartite graph of the formula excludes\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(K_{d,d}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    as an induced subgraph). We show that for every\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\epsilon &gt; 0\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , there exists an algorithm for\n                    <jats:sc>CC-Max-Sat<\/jats:sc>\n                    on\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(K_{d,d}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -free formulas with approximation ratio\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\((1-\\epsilon)\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and running in time\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(2^{{\\mathcal{O}}((\\frac{dk}{\\epsilon})^{d})}(n+m)^{{\\mathcal{O}}(1)}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (these algorithms are called FPT-AS). For\n                    <jats:sc>Maximum Coverage<\/jats:sc>\n                    on\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(K_{d,d}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -free set families, we obtain FPT-AS with running time\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\((\\frac{dk}{\\epsilon})^{{\\mathcal{O}}(dk)}n^{{\\mathcal{O}}(1)}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    .\n                  <\/jats:p>\n                  <jats:p>\n                    Our second result considers \u201coptimizing\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ,\u201d with fixed covering constraint for the\n                    <jats:sc>Maximum Coverage<\/jats:sc>\n                    problem. To explain our result, we first recast the\n                    <jats:sc>Maximum Coverage<\/jats:sc>\n                    problem as the\n                    <jats:sc>Max Red Blue Dominating Set with Covering Constraint<\/jats:sc>\n                    problem. Here, the input is a bipartite graph\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(G=(A,B,E)\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , a positive integer\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( t \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , and the objective is to find a minimum sized subset\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(S\\subseteq A\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , such that\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(|N(S)|\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (the size of the set of neighbors of\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( S \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) is at least\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( t \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    . We design an additive approximation algorithm for\n                    <jats:sc>Max Red Blue Dominating Set with Covering Constraint<\/jats:sc>\n                    , on\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(K_{d,d}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -free bipartite graphs, running in FPT time. In particular, if\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    denotes the minimum size of\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(S\\subseteq A\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , such that\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(|N(S)|\\geq t\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , then our algorithm runs in time\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\((kd)^{{\\mathcal{O}}(kd)}n^{{\\mathcal{O}}{(1)}}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and returns a set\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(S^{\\prime}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    such that\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(|N(S^{\\prime})|\\geq t\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(|S^{\\prime}|\\leq k+1\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    . This is in sharp contrast to the fact that, even a special case of our problem, namely, the\n                    <jats:sc>Partial Vertex Cover<\/jats:sc>\n                    problem (or\n                    <jats:sc>Max<\/jats:sc>\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    <jats:sc>-VC<\/jats:sc>\n                    ) is W[1]-hard, parameterized by\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    . Thus, we get the best possible parameterized approximation algorithm for the\n                    <jats:sc>Maximum Coverage<\/jats:sc>\n                    problem on\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(K_{d,d}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -free bipartite graphs.\n                  <\/jats:p>","DOI":"10.1145\/3763238","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T15:07:51Z","timestamp":1756220871000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameterized Approximation Schemes for Biclique-Free Max\n                    <i>k<\/i>\n                    -Weight SAT and Max 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