{"id":{"repo_id":"usm","oai_identifier":"oai:aquila.usm.edu:masters_theses-1510"},"canonical_url":"https://search.dev.ndltd.org/etd/usm/oai:aquila.usm.edu:masters_theses-1510","repository":{"repo_id":"usm","name":"University of Southern Mississippi","base_url":"https://aquila.usm.edu/do/oai/"},"display":{"title":"Stationary Density Computation of the Frobenius-Perron Operators Based on the Dirac Delta Function","abstract":"<p>The statistical study of chaotic dynamical systems has received a great deal of attention in the past several decades. As a branch of applied mathematics, its application has been found in various fields in science and engineering, while the theory and methods for the existence and computation of absolutely invariant measures have played an important role in this field. In this study, we focus on the computation of a nontrivial fixed point of Frobenius-Perron operators (F-P operators).</p> <p>Let <em>S</em>: [0,1] → [0,1] be a piecewise monotonic mapping, and let <em>P<sub>S</sub></em> : [0,1] → [0,1] be the Frobenius-Perron operators associated with <em>S</em>, which is defined by</p> <p><em>P<sub>S</sub>f</em>(<em>x</em>) = d/dx ∫<sub>S<sup>-1</sup>([0,<em>x</em>])</sub><em> fdm</em>, <em>x</em> ∈ [0,1] <em>a.e.</em>, </p> <p>where <em>m</em> is the Lebesgue measure of [0,1]. Suppose that <em>P<sub>S</sub></em>: [0,1] → [0,1] has a stationary density <em>f</em>*. By using Ulam's method, which he proposed based on a probability argument, approximating the fixed density function <em>f</em>* can be constructed by piecewise constant functions with respect to a partition of [0,1]. From another argument, we propose a different form for the definition of the Frobenius-Penon operator by combining the properties of the Dirac delta function. We can prove that the two definitions for Frobenius-Perron operators are equivalent. Then, we find that by approximating the Dirac delta function, we can exactly obtain the famous Ulam's method again. For the computation of fixed density functions we use the quasi- Monte Carlo method. We partition [0,1] into <em>n</em> subintervals, and for each subinterval we take <em>N</em> equal distance test points. Numerical results are given for several one dimensional test mappings.</p>","abstract_html":"&lt;p&gt;The statistical study of chaotic dynamical systems has received a great deal of attention in the past several decades. As a branch of applied mathematics, its application has been found in various fields in science and engineering, while the theory and methods for the existence and computation of absolutely invariant measures have played an important role in this field. In this study, we focus on the computation of a nontrivial fixed point of Frobenius-Perron operators (F-P operators).&lt;/p&gt; &lt;p&gt;Let &lt;em&gt;S&lt;/em&gt;: [0,1] → [0,1] be a piecewise monotonic mapping, and let &lt;em&gt;P&lt;sub&gt;S&lt;/sub&gt;&lt;/em&gt; : [0,1] → [0,1] be the Frobenius-Perron operators associated with &lt;em&gt;S&lt;/em&gt;, which is defined by&lt;/p&gt; &lt;p&gt;&lt;em&gt;P&lt;sub&gt;S&lt;/sub&gt;f&lt;/em&gt;(&lt;em&gt;x&lt;/em&gt;) = d/dx ∫&lt;sub&gt;S&lt;sup&gt;-1&lt;/sup&gt;([0,&lt;em&gt;x&lt;/em&gt;])&lt;/sub&gt;&lt;em&gt; fdm&lt;/em&gt;, &lt;em&gt;x&lt;/em&gt; ∈ [0,1] &lt;em&gt;a.e.&lt;/em&gt;, &lt;/p&gt; &lt;p&gt;where &lt;em&gt;m&lt;/em&gt; is the Lebesgue measure of [0,1]. Suppose that &lt;em&gt;P&lt;sub&gt;S&lt;/sub&gt;&lt;/em&gt;: [0,1] → [0,1] has a stationary density &lt;em&gt;f&lt;/em&gt;*. By using Ulam&#x27;s method, which he proposed based on a probability argument, approximating the fixed density function &lt;em&gt;f&lt;/em&gt;* can be constructed by piecewise constant functions with respect to a partition of [0,1]. From another argument, we propose a different form for the definition of the Frobenius-Penon operator by combining the properties of the Dirac delta function. We can prove that the two definitions for Frobenius-Perron operators are equivalent. Then, we find that by approximating the Dirac delta function, we can exactly obtain the famous Ulam&#x27;s method again. For the computation of fixed density functions we use the quasi- Monte Carlo method. We partition [0,1] into &lt;em&gt;n&lt;/em&gt; subintervals, and for each subinterval we take &lt;em&gt;N&lt;/em&gt; equal distance test points. Numerical results are given for several one dimensional test mappings.&lt;/p&gt;","abstract_has_math":false,"creators":["Chen, Suanrong"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":"Mathematics","degree_department":null,"school":null,"contributors":["Jiu Ding","Joseph Kolibal","Haiyan Tian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-01T07:00:00Z","date_published":"2011-05-01T07:00:00Z","updated_at":"2026-07-24T05:44:58Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/445","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jiu Ding","Joseph Kolibal","Haiyan Tian"]},{"key":"dc:creator","label":"Author","values":["Chen, Suanrong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-11-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/445"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["<p>The statistical study of chaotic dynamical systems has received a great deal of attention in the past several decades. As a branch of applied mathematics, its application has been found in various fields in science and engineering, while the theory and methods for the existence and computation of absolutely invariant measures have played an important role in this field. In this study, we focus on the computation of a nontrivial fixed point of Frobenius-Perron operators (F-P operators).</p> <p>Let <em>S</em>: [0,1] → [0,1] be a piecewise monotonic mapping, and let <em>P<sub>S</sub></em> : [0,1] → [0,1] be the Frobenius-Perron operators associated with <em>S</em>, which is defined by</p> <p><em>P<sub>S</sub>f</em>(<em>x</em>) = d/dx ∫<sub>S<sup>-1</sup>([0,<em>x</em>])</sub><em> fdm</em>, <em>x</em> ∈ [0,1] <em>a.e.</em>, </p> <p>where <em>m</em> is the Lebesgue measure of [0,1]. Suppose that <em>P<sub>S</sub></em>: [0,1] → [0,1] has a stationary density <em>f</em>*. By using Ulam's method, which he proposed based on a probability argument, approximating the fixed density function <em>f</em>* can be constructed by piecewise constant functions with respect to a partition of [0,1]. From another argument, we propose a different form for the definition of the Frobenius-Penon operator by combining the properties of the Dirac delta function. We can prove that the two definitions for Frobenius-Perron operators are equivalent. Then, we find that by approximating the Dirac delta function, we can exactly obtain the famous Ulam's method again. For the computation of fixed density functions we use the quasi- Monte Carlo method. We partition [0,1] into <em>n</em> subintervals, and for each subinterval we take <em>N</em> equal distance test points. Numerical results are given for several one dimensional test mappings.</p>"]},{"key":"dc:title","label":"Title","values":["Stationary Density Computation of the Frobenius-Perron Operators Based on the Dirac Delta Function"]}]}],"canonical_facts":{"dc:contributor":["Jiu Ding","Joseph Kolibal","Haiyan Tian"],"dc:creator":["Chen, Suanrong"],"dc:date.available":["2018-11-01T07:00:00Z"],"dc:description.abstract":["<p>The statistical study of chaotic dynamical systems has received a great deal of attention in the past several decades. As a branch of applied mathematics, its application has been found in various fields in science and engineering, while the theory and methods for the existence and computation of absolutely invariant measures have played an important role in this field. In this study, we focus on the computation of a nontrivial fixed point of Frobenius-Perron operators (F-P operators).</p> <p>Let <em>S</em>: [0,1] → [0,1] be a piecewise monotonic mapping, and let <em>P<sub>S</sub></em> : [0,1] → [0,1] be the Frobenius-Perron operators associated with <em>S</em>, which is defined by</p> <p><em>P<sub>S</sub>f</em>(<em>x</em>) = d/dx ∫<sub>S<sup>-1</sup>([0,<em>x</em>])</sub><em> fdm</em>, <em>x</em> ∈ [0,1] <em>a.e.</em>, </p> <p>where <em>m</em> is the Lebesgue measure of [0,1]. Suppose that <em>P<sub>S</sub></em>: [0,1] → [0,1] has a stationary density <em>f</em>*. By using Ulam's method, which he proposed based on a probability argument, approximating the fixed density function <em>f</em>* can be constructed by piecewise constant functions with respect to a partition of [0,1]. From another argument, we propose a different form for the definition of the Frobenius-Penon operator by combining the properties of the Dirac delta function. We can prove that the two definitions for Frobenius-Perron operators are equivalent. Then, we find that by approximating the Dirac delta function, we can exactly obtain the famous Ulam's method again. For the computation of fixed density functions we use the quasi- Monte Carlo method. We partition [0,1] into <em>n</em> subintervals, and for each subinterval we take <em>N</em> equal distance test points. Numerical results are given for several one dimensional test mappings.</p>"],"dc:identifier":["https://aquila.usm.edu/masters_theses/445"],"dc:title":["Stationary Density Computation of the Frobenius-Perron Operators Based on the Dirac Delta Function"],"thesis:degree_discipline":["Mathematics"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:44:58Z"}