{"id":13048,"date":"2021-12-05T20:24:09","date_gmt":"2021-12-06T01:24:09","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=13048"},"modified":"2026-06-02T14:59:24","modified_gmt":"2026-06-02T18:59:24","slug":"tr-01-04-a-formal-analysis-of-why-heuristics-work","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2001\/tr-01-04-a-formal-analysis-of-why-heuristics-work\/","title":{"rendered":"TR-01-04: A Formal Analysis of Why Heuristics Work"},"content":{"rendered":"\n<section class=\"w-screen px-6 cu-section cu-section--white ml-offset-center md:px-8 lg:px-14\">\n    <div class=\"space-y-6 cu-max-w-child-5xl  md:space-y-10 cu-prose-first-last\">\n\n            <div class=\"cu-textmedia flex flex-col lg:flex-row mx-auto gap-6 md:gap-10 my-6 md:my-12 first:mt-0 max-w-5xl\">\n        <div class=\"justify-start cu-textmedia-content cu-prose-first-last\" style=\"flex: 0 0 100%;\">\n            <header class=\"font-light prose-xl cu-pageheader md:prose-2xl cu-component-updated cu-prose-first-last\">\n                                    <h1 class=\"cu-prose-first-last font-semibold !mt-2 mb-4 md:mb-6 relative after:absolute after:h-px after:bottom-0 after:bg-cu-red after:left-px text-3xl md:text-4xl lg:text-5xl lg:leading-[3.5rem] pb-5 after:w-10 text-cu-black-700 not-prose\">\n                        TR-01-04: A Formal Analysis of Why Heuristics Work\n                    <\/h1>\n                \n                                \n                            <\/header>\n\n                    <\/div>\n\n            <\/div>\n\n    <\/div>\n<\/section>\n\n<p>Carleton University<br>\n<a href=\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2001\/\">Technical Report<\/a> TR-01-04<br>\nJuly 2001<\/p>\n\n\n\n<h2 id=\"a-formal-analysis-of-why-heuristics-work\" class=\"wp-block-heading\">A Formal Analysis of Why Heuristics Work<\/h2>\n\n\n\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">\n<div class=\"tr_t3\">B. John Oommen &amp; Luis G. Rueda<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div>\n<h3>Abstract<\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<p>Many optimization problems in computer science have been proven to be NP-hard, and hence there are no general polynomial-time algorithms to solve them. Alternatively, they are solved using heuristics, providing a sub-optimal solution that, hopefully, is arbitrarily close to the optimal one. Such problems are found in a wide range of applications, including arti\u001ccial intelligence, game theory, graph partitioning, database query optimization, etc. Given two heuristics, the question of determining which is superior, has typically demanded a yes\/no answer which is often substantiated based on empirical evidence. We have solved the problem of deciding on the superior heuristic by using Pattern Classi\u001ccation Techniques (PCT). We prove the following assertion: Given two heuristics H1 and H2 used in determining the goal of a particular problem, if the accuracy in obtaining the optimal solution by H1 is greater than that of H2, then H1 has a higher probability of leading to the optimal solution than H2. To the best of our knowledge, this is an open problem; this unproven conjecture has been the basis for designing numerous algorithms such as the A* algorithm, and its variants. By formulating the problem from a Pattern Recognition perspective, we use PCT to present a mathematical, rigorous proof of this fact, and show some uniqueness results. The corresponding database query optimization problem has been open for at least two decades, the di\u001eculty of which has partially been due to the hurdles involved in the formulation itself. To validate our proofs, we report empirical results on database query optimization techniques involving a few well-known histogram estimation methods.<\/p>\n\n\n\n<p><a href=\"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/sites\/260\/TR-01-04.pdf\">TR-01-04.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Carleton University Technical Report TR-01-04 July 2001 A Formal Analysis of Why Heuristics Work B. John Oommen &amp; Luis G. Rueda Abstract Many optimization problems in computer science have been proven to be NP-hard, and hence there are no general polynomial-time algorithms to solve them. Alternatively, they are solved using heuristics, providing a sub-optimal solution [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":12272,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_cu_dining_location_slug":"","footnotes":"","_links_to":"","_links_to_target":""},"cu_page_type":[],"class_list":["post-13048","page","type-page","status-publish","hentry"],"acf":{"cu_post_thumbnail":false},"_links":{"self":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/13048","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/comments?post=13048"}],"version-history":[{"count":1,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/13048\/revisions"}],"predecessor-version":[{"id":13049,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/13048\/revisions\/13049"}],"up":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/12272"}],"wp:attachment":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/media?parent=13048"}],"wp:term":[{"taxonomy":"cu_page_type","embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/cu_page_type?post=13048"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}