Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with linear-programming
Search options not deleted
user 9022
Linear programming is the study of optimizing a linear function over a set of linear inequalities. The Simplex Method, Ellipsoid Method and Interior Point Method are popular algorithms to solve linear programs.
9
votes
How do you tell if a system of linear inequalities has a solution?
You haven't said whether you're interested in theoretical analysis or practical computation. At the practical level, you also haven't specified whether you can live with an approximate floating poin …
- 3,934
6
votes
Why are optimization problems often called "programs"?
When the term "linear programming" first came into use, computers were still very rare beasts, and the term "computer programming" wasn't that widely used. Here "programming" meant planning. As rese …
- 3,934
6
votes
efficient way to compute the inversion of the following matrix
As others have already indicated, there's a good chance that you don't actually need $(E-aX)^{-1}$, but rather $(E-aX)^{-1}v$ for some vector $v$. In that case, you might be better off using an itera …
- 3,934
4
votes
Accepted
Linear program to maximize the minimum absolute value of linear functions ?
Unfortunately, this problem can't be represented by an LP, since your feasible region is in general nonconvex, and the feasible region of an LP (being the intersection of a bunch of half spaces) is al …
- 3,934
4
votes
solving multiple linear programming problems with the same set of constraints
Where do the different objective functions come from? Perhaps if we knew more about your problem we could make a helpful suggestion about how to approach it.
If the objective functions are totall …
- 3,934
3
votes
Accepted
Cascading minimization problems
You can solve the first LP, obtain the optimal value, and then add a constraint that the first objective has that optimal value. Then you can add your second set of constraints to the original set of …
- 3,934
3
votes
Accepted
linear programming with OR restrictions
Your additional constraints make the feasible region for your problem nonconvex, and thus it cannot be represented as a linear programming problem.
If you can obtain an upper bound $M_{i}$ on the max …
- 3,934
2
votes
Sensitivity analysis in conic optimization
In general, your problem can become infeasible under arbitrarily small perturbations $\Delta b$. For example, consider the problem
$\min X_{11}+X_{22}$
subject to
$X_{11}=0$
$X_{22}=0$
$X \suc …
- 3,934
2
votes
Levenberg-Marquadt near the minima for non-zero-residual problems
The technique of multiplying the diagonal by $(1+\lambda)$ fails when you've got a 0 on the diagonal of $J^{T}J$. Having a zero on the diagonal of $J^{T}J$ can happen when you're far away from the op …
- 3,934
2
votes
Accepted
Model for shipping widgets in an optimal way
Is the routing from factory to distributor to location fixed, or are you considering optimizing this too? In particular, are there costs associated with using a distributor? If the answers to one or …
- 3,934
2
votes
Accepted
Sherali-Adams relaxation
It isn't clear from your posting whether you're trying to understand:
Why the inequalities generated by the Sherali-Adams procedure are valid?
or
Why the procedure is complete in the sense that af …
- 3,934
1
vote
Accepted
Large scale least squares of non symmetric and non square problems
A widely used iterative method for large scale linear least squares problems that allows for regularization if you want/need it is the LSQR algorithm of Paige and Saunders. See
http://www.stanford.ed …
- 3,934
1
vote
positive semidefiniteness: a psd matrix substracted by another rank 1 psd matrix
This is easy to formulate as a semidefinite programming problem.
First, let $X=xx^{T}$. The semidefiniteness constraint becomes
$A-\lambda X \succeq 0$
Next, use a standard technique to handle t …
- 3,934
1
vote
Rewrite optimization objective
You need to start by by being explicit about the "equivalence" between the problems. Do you mean that "For every $s$, there is a $t$ such that if $x^{ * }$ is an optimal solution to the first problem …
- 3,934
1
vote
Accepted
Rewrite optimization objective
Here's a solution for the specific case where $f(x)=\| x \|_{1}$ and $g(x)=\| y-Ax \|_{2}$. The same appraoch applies to $g(x)=\| A^{T}(y-Ax) \|_{\infty}$.
There are two cases.
If $g(0) \leq s$, …
- 3,934