Questions tagged [global-optimality]
Use this tag for questions relating to an optimal (best) solution which attains the best objective function value over the entire set of feasible solutions.
13
questions
1
vote
0answers
41 views
The different behavior of the solvers to dial with MINLP problem
I have tried to solve an optimization problem (an MINLP) to minimize the number of items which need to be stored. The objective function is as follows:
$$\min \quad z = \sum_{i=1}^{n} \, \color{blue} {...
0
votes
1answer
46 views
What is a good approach to deciding which jobs (from a list of HPC jobs) should be ran locally vs. on the cloud given time & cost constraints?
Cloud computing has transformed the landscape of compute operations. Of course, there are still many labs/businesses with local, large-scale compute clusters. For those businesses who keep the ...
5
votes
2answers
103 views
how to determine differents gap rate?
I found in the literature different gaps:
a gap between a random solution and an exact solution
a gap between the exact solution and a lower bound
a gap between the exact solution and a lower bound
a ...
4
votes
1answer
71 views
the set of optimal solutions of a linear programming (LP) problem as a mapping of right-hand side
Consider a linear programming (LP) problem
\begin{align}
M(b) \in \arg\min_x \{ c^\top x : Ax=b, x \ge 0 \}.
\end{align}
Suppose the LP is feasible and bounded for all values of $b$. We know that $M(...
3
votes
1answer
45 views
Is Multidisciplinary Design Optimization / Collaborative Optimization used anywhere outside of the Mechanical Engineering context?
I recently stumbled across the concept of Multidisciplinary Design Optimization (MDO), sometimes referred to as Multi-Disciplinary Optimization or Multidisciplinary Systems Design Optimization (MSDO), ...
3
votes
1answer
111 views
Radial unboundedness vs convexity
We have a simple problem, namely minimizing:
$$f(x) = x_1^2 + x_2^2 - x_1.$$
The gradient is $$\nabla f(x) = \begin{bmatrix} 2x_1 - 1 \\ 2x_2 \end{bmatrix},$$
hence the unique stationary point is: $...
3
votes
0answers
40 views
Derivations for two formulae for obtaining optimal dual variable values from the optimal primal tableau
We're being taught Industrial Engineering and Operations Research for the first time this semester. Referring to the book by Hamdy A. Taha, I noticed the mention of two formulae for swiftly obtaining ...
9
votes
1answer
87 views
Problem solvable $\Rightarrow$ subproblems solvable if feasible region closed, convex?
Let $c \in \mathbb{R}^n$, $M \subseteq \mathbb{R}^n$ such that the problem
\begin{align}P:\quad\min_{x \in \mathbb{R}^n}&\quad c^\intercal x\\\textrm{s.t.}&\quad x \in M\end{align} is solvable....
11
votes
1answer
440 views
Solvers and saddle points
It seems like most solvers that can tackle nonlinear nonconvex optimization problems (e.g. IPOPT) operate on ultimately solving for the first-order optimality conditions. Can it therefore be assumed ...
11
votes
1answer
186 views
Recovering primal optimal solutions from dual sub gradient ascent using ergodic primal sequences
My question concerns recovering a primal optimal solution while performing dual sub gradient ascent. Denoting by $y_i$ the dual multiplier in the $i^{\text{th}}$ iteration, let
\begin{equation}
x_i = ...
22
votes
4answers
2k views
What is a solution?
Consider a standard optimization problem: Minimize an objective function with respect to constraints. My question is:
What does the term "solution of the optimization problem" mean?
At first I ...
10
votes
1answer
378 views
Termination Criteria of Solver in Pyomo
I am solving a nonlinear optimization problem using Pyomo with Ipopt as solver. The solver exits with the status:
EXIT: Optimal Solution Found.
This I can cross ...
14
votes
4answers
829 views
CPLEX non-convex Quadratic Programming algorithms
CPLEX solves non-convex quadratic problems to global optimality with a global optimality option (in version 12). The relevant pages are
this and this. I benchmarked many solvers, and see that CPLEX ...
