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      <title>Branch and Bound</title>
      <dc:creator>221910301004</dc:creator>
      <pubDate>Mon, 10 May 2021 03:14:50 +0000</pubDate>
      <link>https://dev.to/221910301004/branch-and-bound-1lbi</link>
      <guid>https://dev.to/221910301004/branch-and-bound-1lbi</guid>
      <description>&lt;h1&gt;
  
  
  Intro to Branch and Bound :
&lt;/h1&gt;

&lt;p&gt;Branch and bound is a systematic method for solving optimization problems&lt;br&gt;
B&amp;amp;B is a rather general optimization technique that applies where the greedy method and dynamic programming fail.&lt;br&gt;
However, it is much slower. Indeed, it often leads to exponential time complexities in the worst case.&lt;br&gt;
On the other hand, if applied carefully, it can lead to algorithms that run reasonably fast on average.&lt;br&gt;
The general idea of B&amp;amp;B is a BFS-like search for the optimal solution, but not all nodes get expanded (i.e., their children generated). Rather, a carefully selected criterion determines which node to expand and when, and another criterion tells the algorithm when an optimal solution has been found.&lt;/p&gt;

&lt;h1&gt;
  
  
  Uses of Branch and Bound :
&lt;/h1&gt;

&lt;p&gt;Branch and bound algorithms are used to find the optimal solution for combinatory, discrete, and general mathematical optimization problems. In general, given an NP-Hard problem, a branch and bound algorithm explores the entire search space of possible solutions and provides an optimal solution.&lt;/p&gt;

&lt;h1&gt;
  
  
  Algorithm :
&lt;/h1&gt;



&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight plaintext"&gt;&lt;code&gt;Set L = {X} and initial ize x
2  while L # 0 :
3   Select a subproblem S from L to explore
4   if a solution.r' E {x E S I f (x) &amp;lt; /(x)} can be found: Set .X =x'
s   if S cannot be pmned:
6   Partition S imo S1 , S2 , ... , S,. Insert S1, S2, . . . , S, into L
8   Remove S from L
9 Return x

&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;h1&gt;
  
  
  conclusion :
&lt;/h1&gt;

&lt;p&gt;cutting planes can reduce the search space and thus improve the lower bounds on solutions of mixed integer linear programs. When using cutting planes, the branch-and-bound algorithm is also called the branch-and-cut algorithm. Preprocessing can reduce problem size and improve problem solvability. &lt;/p&gt;

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