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In this work, we generalize those results to the weighted setting. More formally, we consider monotone subset minimization problems over a weighted universe of size\n                    <jats:italic>n<\/jats:italic>\n                    (e.g.,\n                    <jats:sc>Vertex Cover<\/jats:sc>\n                    ,\n                    <jats:italic>d<\/jats:italic>\n                    <jats:sc>-Hitting Set<\/jats:sc>\n                    and\n                    <jats:sc>Feedback Vertex Set<\/jats:sc>\n                    ). We consider a model where the algorithm is only given access to a subroutine that finds a solution of weight at most\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\alpha \\cdot W$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>\u03b1<\/mml:mi>\n                            <mml:mo>\u00b7<\/mml:mo>\n                            <mml:mi>W<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    (and of arbitrary cardinality) in time\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$c^k \\cdot n^{\\mathcal {O}(1)}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:msup>\n                              <mml:mi>c<\/mml:mi>\n                              <mml:mi>k<\/mml:mi>\n                            <\/mml:msup>\n                            <mml:mo>\u00b7<\/mml:mo>\n                            <mml:msup>\n                              <mml:mi>n<\/mml:mi>\n                              <mml:mrow>\n                                <mml:mi>O<\/mml:mi>\n                                <mml:mo>(<\/mml:mo>\n                                <mml:mn>1<\/mml:mn>\n                                <mml:mo>)<\/mml:mo>\n                              <\/mml:mrow>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    where\u00a0\n                    <jats:italic>W<\/jats:italic>\n                    is the minimum weight of a solution of cardinality at most\n                    <jats:italic>k<\/jats:italic>\n                    . In the unweighted setting, Esmer et al. determine the smallest value\n                    <jats:italic>d<\/jats:italic>\n                    for which a\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b2<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -approximation algorithm running in time\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$d^{n + o(n)}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>d<\/mml:mi>\n                            <mml:mrow>\n                              <mml:mi>n<\/mml:mi>\n                              <mml:mo>+<\/mml:mo>\n                              <mml:mi>o<\/mml:mi>\n                              <mml:mo>(<\/mml:mo>\n                              <mml:mi>n<\/mml:mi>\n                              <mml:mo>)<\/mml:mo>\n                            <\/mml:mrow>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    can be obtained in this model. We show that the same dependencies also hold in a weighted setting in this model: we obtain a\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b2<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -approximation algorithm running in time\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$d^{n + o(n)}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>d<\/mml:mi>\n                            <mml:mrow>\n                              <mml:mi>n<\/mml:mi>\n                              <mml:mo>+<\/mml:mo>\n                              <mml:mi>o<\/mml:mi>\n                              <mml:mo>(<\/mml:mo>\n                              <mml:mi>n<\/mml:mi>\n                              <mml:mo>)<\/mml:mo>\n                            <\/mml:mrow>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , for the same\n                    <jats:italic>d<\/jats:italic>\n                    as in the unweighted setting. Similarly, we also extend a\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b2<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -approximate brute-force search (in a model which only provides access to a membership oracle) to the weighted setting. Using existing approximation algorithms and exact parameterized algorithms for weighted problems, we obtain the first exponential-time\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\beta $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b2<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -approximation algorithms that are better than brute force for a variety of problems including\n                    <jats:sc>Weighted Vertex Cover<\/jats:sc>\n                    ,\n                    <jats:sc>Weighted<\/jats:sc>\n                    <jats:italic>d<\/jats:italic>\n                    <jats:sc>-Hitting Set<\/jats:sc>\n                    ,\n                    <jats:sc>Weighted Feedback Vertex Set<\/jats:sc>\n                    and\n                    <jats:sc>Weighted Multicut<\/jats:sc>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s00453-026-01402-7","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:07:08Z","timestamp":1787026028000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Approximate Monotone Local Search for Weighted Problems"],"prefix":"10.1007","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5694-1465","authenticated-orcid":false,"given":"Bar\u0131\u015f Can","family":"Esmer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0533-3926","authenticated-orcid":false,"given":"Ariel","family":"Kulik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D\u00e1niel","family":"Marx","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4940-0318","authenticated-orcid":false,"given":"Daniel","family":"Neuen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roohani","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"1402_CR1","doi-asserted-by":"publisher","unstructured":"Fomin, F.V., Kratsch, D.: Exact Exponential Algorithms. 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