Unanswered Questions
78 questions with no upvoted or accepted answers
11
votes
0
answers
164
views
Characterizing the solution of a (non) linear maximization program
I have the following maximization program
\begin{align}
\max\limits_{\{q_i\}}&\quad\sum\limits_{i=1}^nq_i \\
\text{s.t.}&\quad\begin{cases} k_j \geq \sum\limits_{i=1}^n q_i^{1 \over \...
6
votes
0
answers
127
views
Water quality component optimization
I have an optimization problem that I'm attempting to tackle. As you can see in the image below, there's a graph network through which water flows. I've drawn out the problem in the image to explain ...
6
votes
0
answers
227
views
OptionalIntervalVar enforced but not working OR-TOOLS
I'm using OptionalIntervalVar and then maximizing the starts. The optimal solution is not affecting the other variables that creates the ...
6
votes
0
answers
166
views
Modelling issue with precedence constraint in OR-Tools
I'm trying to solve an RCPSPDc model It is infeasible, although Ilog Cplex solves it, so I think I have a modelling issue. I have more constraints but the precedence is the one that makes it ...
6
votes
0
answers
91
views
Data Formulation for Mixed-Integer-Programming Models
Until now, I have used the Gurobi, CPLEX and OR-Tools (GCO) interface to formulate mixed-integer-programming models.
Recently, I have discovered MiniZinc and want to utilize it to formulate big ...
5
votes
0
answers
553
views
How to write this objective in CVXPY for quasiconvex programming?
I have the following objective that I want to maximize:
\begin{equation}
\max_{U_T\in \mathbb{R}, x\in\mathbb{R}^T} J_\alpha(U_T) = \frac{\alpha}{\alpha-1}\log\left(\frac{\cosh(U_T)}{\cosh(\alpha U_T)^...
5
votes
1
answer
162
views
Numerically stable way to optimize a lexicographical preference between two objective functions?
I am solving a mixed-integer program whose decision variables are $x \in \{0, 1\}^n$ and $y \in \mathbb{R}^m$, where $0 \leq y_j \leq u_j$ for constant upper bounds.
My primary objective function is ...
5
votes
0
answers
135
views
Is there a way to use lazy constraints with Baron?
I am solving a non-linear mixed-integer programme with BARON. The objective function looks like $\big( \sum_i x_i \big) \cdot \big(\prod_i e^{-y_i}\big)$ (binary $x$ and real-valued $y$) and it has ...
5
votes
0
answers
44
views
In a binary logistic regression context, how to introduce a constraint to model the dependency between consecutive samples
Imagine we are running a logistic regression to identify opportunities for car sale promotion, using previous promotion campaign's result. Each $y$ is the increase of car sale after the promotion.
...
4
votes
0
answers
1k
views
Google-OR tools vs Pyomo and other commercial Solvers for solving a simple maximum flow problem
I have implemented a Pyomo model for solving maximum flow problem as a subroutine of an algorithm. However, the approach does not scale very well because Pyomo does not provide a very good way to re-...
4
votes
0
answers
107
views
How to linearize or convexify a constraint with a square root of sum of two variables?
Here is the constraint:
$$\text{Pa} + \text{Pb}=a + b \sqrt{\text{Ir}^2 +\text{Ii}^2} + c (\text{Ir}^2 +\text{Ii}^2)$$
Here $\text{Pa}, \text{Pb}, \text{Ir},$ and $\text{Ii}$ are variables. $a, b, c$ ...
4
votes
0
answers
36
views
Does knowing the "correct multipliers" for globally optimal first-order critical points help you algorithmically?
Consider the following nonlinear optimization problem:
\begin{align*}
&\min f(x) \\
\text{such that } &h_1(x) = 0, \\
&h_2(x) = 0, \\
& \vdots \\
& h_m(x) = 0,
\end{align*}
where $...
4
votes
0
answers
288
views
Linearize a highly non-linear objective function
[EDIT] : The formula below is updated to remove the radical, 0.5 in the term $(I_{i,v} \cdot \Delta t)$ and constant temperature $T$ replces temperature as function of current.
[EDIT] :The values of ...
4
votes
0
answers
73
views
How can non-polyhedral sets be investigated?
To derive problem-specific cutting planes for some given problem (think something like TSP problem), one common way is to study small examples. To this end, one can create small instances for the ...
4
votes
0
answers
92
views
Identifying saddle point in constrained optimization
Suppose we are minimizing $f(x)$. The first order necessary condition of $x^*$ being local minmum is:
$$\nabla f(x^*)= \mathbf{0}.$$
For sufficiency, we check if also $\nabla^2f(x^*) \succ 0$, i.e., ...