# Questions tagged [sensitivity-analysis]

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### Pyomo sensitivity report access

I have been learning pyomo with glpk for a week now and I would like to know if pyomo team has developed a way to access "allowable increase" and "allowable decrease" values of the ...
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### Does the value function of a quadratic program stay convex when adding constraints?

I am interested in the value function of a quadratic program of the form $$v(y)=\min_x \frac{1}{2} x^\top Q(y) x,$$ subject to a linear equality constraint $$E(y)x=d(y),$$ and a linear inequality ...
• 121
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### Reading MPS file for linear programming and reconstructing the Optimization model

Are you aware of any tutorial that can help me learn on how to reconstruct the objective function and constraints from a MPS file once it's loaded in MATLAB. I can load the mps file given to me and ...
• 793
164 views

### How to obtain the sensitivity analysis of correlated data?

A company should supply 2 products namely A and B. The company can buy A and B in advance or in real-time, however at different prices. The demand is uncertain and prices are considered to be ...
• 294
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### Waiting time in M/M/n queue

I was able to numerically "show" that an M/M/n queue is concave increasing in the number for $n$ servers, which makes intuitive sense. However is there a formal analytical proof for it? Where can I ...
• 123
933 views

### Linear programming sensitivity analysis using Matlab

I have a linear program in the MPS file format listing all the rows, columns, right-hand sides, etc. I can read that in Matlab and solve it using linprog. However, it seems there is no easy way to do ...
• 793
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### Negative reduced cost for basic variable

I am observing something unusual : after solving a linear program, some basic variables have negative reduced costs (instead of $0$) : ...
• 13.2k
161 views

### Variable Sensitivity Analysis

I am working with the following MIP : \begin{alignat}2\min&\quad\sum_{j\in J} c_j x_j\\\text{s.t.}&\quad l_j \le f(x_j,t_j) \le u_j \quad &\forall j \in J \\&\quad x_j \in \mathbb{N} \...
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### Partial derivative of LP solution $(x_1 , \ldots, x_n)$ w.r.t. $x_i$ or $a_i$
Suppose I have an optimal solution and I want to know how the solution would (likely) change if one of the coefficients in the objective function changes, or if I add a constraint that forces $x_i$ ...