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		<title>Start Here on CS 412</title>
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		<description>Recent content in Start Here on CS 412</description>
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				<title>Getting Started</title>
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				<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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				<description>&lt;blockquote&gt;&#xA;&lt;p&gt;&lt;strong&gt;Goal:&lt;/strong&gt; Form your team and submit your three ranked project preferences.&lt;br&gt;&#xA;&lt;strong&gt;Points:&lt;/strong&gt; 5 · &lt;strong&gt;Submission:&lt;/strong&gt; Gradescope&lt;/p&gt;&#xA;&lt;/blockquote&gt;&#xA;&lt;p&gt;Project Selection comes &lt;strong&gt;before Checkpoint 1&lt;/strong&gt;. Follow the four steps below in order.&lt;/p&gt;&#xA;&lt;h2 id=&#34;1-form-your-team&#34;&gt;1. Form your team&lt;/h2&gt;&#xA;&lt;p&gt;Work in a team of &lt;strong&gt;two or three students&lt;/strong&gt;, and choose a team name. Students who have &lt;strong&gt;instructor approval to work individually&lt;/strong&gt; may submit as a one-person team. You will need the name of every team member for your submission.&lt;/p&gt;</description>
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				<title>Learning Objectives</title>
				<link>/molloykp/teaching/cs412_f26/pas/npcompleteproject/start-here/learning-objectives/</link>
				<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
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				<description>&lt;p&gt;By completing &lt;strong&gt;Beyond Brute Force&lt;/strong&gt;, you should be able to:&lt;/p&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;explain the relationship between an NP-complete decision problem and its optimization version;&lt;/li&gt;&#xA;&lt;li&gt;identify what constitutes a feasible solution and explain how a proposed solution can be verified efficiently;&lt;/li&gt;&#xA;&lt;li&gt;implement and analyze a straightforward exhaustive exact algorithm;&lt;/li&gt;&#xA;&lt;li&gt;reason about the state of an exact search and whether repeated subproblems are present;&lt;/li&gt;&#xA;&lt;li&gt;improve exact computation using an appropriate algorithm-design technique while preserving optimality;&lt;/li&gt;&#xA;&lt;li&gt;construct and justify a valid polynomial-time bound for an optimization problem;&lt;/li&gt;&#xA;&lt;li&gt;design and evaluate a polynomial-time heuristic for instances where exact computation becomes impractical;&lt;/li&gt;&#xA;&lt;li&gt;use reproducible randomized experiments and best-so-far behavior to evaluate heuristic approaches;&lt;/li&gt;&#xA;&lt;li&gt;compare heuristic results with known optima when available and with valid bounds or best-known solutions when OPT is unknown;&lt;/li&gt;&#xA;&lt;li&gt;perform controlled experiments across a collection of problem instances;&lt;/li&gt;&#xA;&lt;li&gt;identify input characteristics that affect computational effort or solution quality; and&lt;/li&gt;&#xA;&lt;li&gt;communicate algorithmic conclusions using appropriately qualified experimental evidence.&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;h2 id=&#34;key-terminology&#34;&gt;Key terminology&lt;/h2&gt;&#xA;&lt;p&gt;The theoretical portion of this project focuses on the &lt;strong&gt;decision version&lt;/strong&gt; of your problem, while the programming and experimental portions focus on the corresponding &lt;strong&gt;optimization version&lt;/strong&gt;.&lt;/p&gt;</description>
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