Topic
  • 2 replies
  • Latest Post - ‏2013-09-10T14:16:44Z by taidungman
taidungman
taidungman
2 Posts

Pinned topic solving multiple small problems vs. one large problem

‏2013-09-05T01:34:46Z |

Hello everyone,

Recently I did a simulation experiment for a particular LP. In each iteration I need to solve many instances. Say, Max(C_i*X_i| X_1 in A_i), where X_1 is a vector of decision variable and A1 is the feasible set for iteration i, i=1,...,n.

Instead of solving n small problems, I can obtain the same results by solving a large combined problem

Max(\sum_i C_i*X_i| X_i in A_i for i=1,...,n)

 

Then, much to my surprise, I discovered that when "n" is not too large, it's quicker to solve the large problem instead of multiple small problems. Since I know little about how the algorithm in modern software packages actually works (by the way, I used MATLAB for the experiment), I'm wondering if anyone has any explanation for this? 

I'm guessing the pre-processing steps (just like the idea of fixed costs) slows down the process of solving multiple problems, but I'm totally unsure. Thanks. 

Updated on 2013-09-05T01:39:00Z at 2013-09-05T01:39:00Z by taidungman
  • GGR
    GGR
    44 Posts

    Re: solving multiple small problems vs. one large problem

    ‏2013-09-09T17:31:29Z  

    Hi

     

    This forum is about constraint programming. For linear Programming issues please post on one of the forums in

     

    IBM ILOG >‎ IBM ILOG Optimization >‎ Mathematical Programming >‎

     

    Hope that helps

     

  • taidungman
    taidungman
    2 Posts

    Re: solving multiple small problems vs. one large problem

    ‏2013-09-10T14:16:44Z  
    • GGR
    • ‏2013-09-09T17:31:29Z

    Hi

     

    This forum is about constraint programming. For linear Programming issues please post on one of the forums in

     

    IBM ILOG >‎ IBM ILOG Optimization >‎ Mathematical Programming >‎

     

    Hope that helps

     

    Thanks. Will do.