Questions tagged [nonconvex-programming]
For questions about non-convex optimization problems where the objective or any of the constraints are non-convex.
68
questions
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My barrier function is always giving a complex number
I am working on implementing the interior point method, and the barrier function always gives me a complex number. B(x) = f(x) - t * sum(ln(hi(x))). I have changed the value of 't' to see the B(x) ...
1
vote
2
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535
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Non linear programming
I want to solve a large scale non linear optimization problem and there are two methods interior point method and sequential quadric programing usually used to solve non linear optimization problem. I ...
2
votes
1
answer
106
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Formulation of nonlinear nonconvex optimization problem and finding appropiate solver
Consider the notation and objective below for this sequential resource allocation problem:
Allocation channels $i \in (1, 2)$
Spend/Cost timestep i channel j: $C_{i, j}$
Total resource: $B$
Horizon: $...
2
votes
2
answers
240
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An if-then-else logic to construct constraint
I was hoping to get some help in modelling the following logic, I know that it would use some kind of Big M formulation but I am not sure how. Thank you in advance!
$\Omega$ is a set whose values are ...
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1
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89
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Ipopt finds a better solution if I do not eliminate the zeros in the hessian matrix ?(we eliminate the zeros by defining the structure)
I use Ipopt to solve a problem with sparse hessian and jacobian matrices.
If I provide the hessian matrix: its structure, and the non zeros elements in the hessian matrix, it will be really fast.
If I ...
- 391
0
votes
1
answer
58
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The max_wall_time and max_cpu_time in ipopt are not working?
The max_wall_time and max_cpu_time are not working in ipopt (cyipopt).
See example:
...
- 391
0
votes
0
answers
50
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Can we use the Jax library with ipopt to calculate the hessian matrix and Jacobian and still be able to define the structures?
When solve with ipopt, we can use Jax to calculate the hessian matrix and jacobian instead of providing it ourselves. However, ipopt with Jax is very slow for large problems.
If we calculate the ...
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162
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Should I provide the hessian matrix, hessian structure, and Jacobian structure if I use cyipopt (IPOPT) if I am concerned about computation time?
If I use IPOPT (cyipopt) to solve nonlinear problems of large scale. It is optional to provide or not provide the hessian matrix, hessian structure, and Jacobian structure.
The question is which one ...
- 391
2
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0
answers
55
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Minimizing sum of similar functions with a dependence
Consider an objective function in the form of minimization of maximization that is the sum of $N$ similar functions $f\left(x\right)\ge 0$, $\ \forall x$. The summation of all variables is constant (e....
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73
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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. $a, b, c$ ...
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2
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99
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Does the cvxpy replace the max function by MIP formulation under the hood?
Does the cvxpy replace the max function, which is convex, by MIP formulation under the hood when shows up in the constraints (for example, $\max(x,y)\le z$) or in the objective function? In gurobipy, ...
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votes
1
answer
292
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Endowment of an agent
I was going through the Shapley-Folkman-Starr Lemma (https://simons.berkeley.edu/sites/default/files/docs/3605/simons2.pdf) and I came across the term "endowment" of an agent.
My assumption ...
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How do we call this optimization problem?
Let $f_k\colon\Bbb R^n\to\Bbb R$, $k=1,\dots,K$, be differentiable (possibly nonconvex) functions and $X\subset\Bbb R^n$ be a convex set.
Consider the following optimization problem:
$$
\min_{x\in X}\...
- 155
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0
answers
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Does Gurobi solve QCQMIPs with Quadratic terms faster with then Bi-Linear terms in general?
Based on the color distance function defined here i try to find $n$ RGB colors with large inter set color distances and good color distance to white.
...
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3
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1
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Methods to solve integer linear inequalities with products of two variables
I'm interested in solving the following system of equations over the integers:
\begin{align*}
x_l^3 &\le x_l^1x_l^2 & \text{ for } l = 1,\ldots,s \\
A x &\le b \\
0 &\le x
\end{align*}
...
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8
votes
1
answer
234
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Maximize correlation subject to nonconvex correlation constraints
Let $r, z$ and each of $u_i$ be a length $n$ vector. I’d like to maximize the correlation between $z$ and $r$ (when that correlation is positive) while keeping $z$ “away” from $u_i$’s. Formally,
\...
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2
votes
0
answers
57
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FOC point vs Stationary point in local optimization
In this SIAM Review paper the authors are giving the following necessary condition for a point being a local maximum of a convex function:
Let $F: \mathbb{R}^n \mapsto \mathbb{R}$ be convex. If $x$ ...
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1
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107
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Convex-Constrained Nonconvex-Nonconcave Minimax Problem
In the mathematical optimization theory, I have taken a glance at many papers which deal with the unconstrained convex-concave or nonconvex-concave minimax optimization, i.e.,
$$
\min_{x\in X}\ \max_{...
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3
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How to find the point on the exterior of a given set of points?
Suppose we do have a set of points (all on a plane ).
How to find the smallest hull containing all these points ?
How to find the points (among these given points) that are at the exterior layers of ...
- 1,076
1
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2
answers
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Branch and bound method for solving non-convex integer non-linear multi-objective optimization problem?
Following are the characteristics of my problem:
Objective function: two non-linear functions and one linear function
Decision variable: two integer variables ($X_1$ and $X_2$)
Constraint: three (two ...
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5
votes
1
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389
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What methods are used to solve multi-objective optimization problem with non-linear objective functions and integer decision variables?
Case 1: NLP
When either the objective function or at least one of the constraints or both are non-linear it is a NLP. We use generalized reduced gradient or Quadratic Programming to solve NLP. However,...
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votes
1
answer
397
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How to linearize a non-convex optimization objective function?
The non-convex multi-objective optimization problem in my case is defined below:
Objective 1: Minimize $f_1(X_1,X_2)=C_0+C_1(1/X_1)+C_2(X_2/X_1)+C_3X_1+C_4X_2+C_5(X_2^2/X_1)$
Objective 2: Minimize $...
- 159
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1
answer
151
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Maximization of a nonconvex bi-variate function
Suppose we have a bi-variate function like $f(x,y)$ which is concave in $x$, $\frac{d^2f(x,y)}{dx^2} = -g(x,y)<0$ (that is $f(x,y)$ can be a function with high order in $x$ ) but convex in $y$, ...
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4
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1
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198
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Is it always possible to optimize a multivariate function sequentially?
Suppose we have a multivariate function like $f(x,y,z)$ which should be maximized with the constraints $g_i(x,y,z)\le 0 \quad \forall i$. The general rule is to use KKT conditions and derive all KKT ...
- 2,140
1
vote
1
answer
99
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Maximization of a differentiable and nonlinear function over a bounded space
I have a nonlinear bi-variate optimization problem like $\max \: f(x,y)$ where $f(x,y)$ is a nonlinear and differentiable function of both variables, and $0\le x\le 1$, $\:0\le y \le ub$. In order to ...
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3
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Non-Convex QCP model - Used Method in Gurobi
I have the following question:
I have a non-convex QCP model. In the parameter description for method it says that "Only barrier is available for continuous QCP models". However, the dual ...
- 291
1
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1
answer
259
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How to convexify log(convex) function?
I have the following optimization problem: \begin{align}\max_x&\quad\log_2(1+|a+bx|^2+cx^2)\\\text{s.t.}&\quad0\le x\le1\\&\quad(1-x^2)\ge\text{constant}\end{align} where $a$ and $b$ are ...
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answer
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Find an upper bound for an objective function
My objective function is $\log_2(1+{x^2y^2})$ and I found two upper bounds for $x^2$ and $y^2$.
For example, assumed that we have the following upper bounds:
$x^2\leq\text{constant}_1^2$ and $y^2\leq\...
1
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1
answer
203
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Non-linear optimization local or global solution
In an NLP, I have a constraint that I would like to formulate in a convex manner preferably without introducing binary variables and/or big M formulations if possible. The actual problem is non-convex ...
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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 ...
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how to implement an optimization function with polynomial in Gurobi (Java)
I have the following problem:
I have an objective function with the optimization variable $x$, which looks simplified like this:
$ZF = (a+b)*(x+1)$
Here $a$ is simply a constant value.
However, behind ...
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1
answer
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Maximizing a piecewise-linear convex function
Note: Initially posted on MathOverflow.
I am working on an optimization problem where some of the terms of the objective function to maximize are expressed as a piecewise linear function of variables:...
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2
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Trustful Nonlinear Programming
Is it possible for an NLP solver to claim that a knowingly feasible problem is infeasible?
Shouldn't the solver be able to provide a solution (of course not necessarily the global optimum but a ...
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1
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Maximize $\sum_{i=1}^n 1/x_i$ subject to an SDP constraint
I would like to solve the following problem: \begin{align}\max_{x_1, \ldots, x_n}&\quad\frac{1}{x_1} + \frac{1}{x_2} + \cdots + \frac{1}{x_n}\\\text{s.t.}&\quad\sum_{i=1}^n x_i A_i \succeq A_0\...
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votes
1
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"Rank 1" type constraint $X=vw^\top$: MILP representation? Convex relaxation? Other tractable approach?
Suppose $X\in\mathbb{R}^{m\times n}$, $v\in\mathbb{R}^m$, $w\in\mathbb{R}^n$ are variables from an optimization problem, which also includes the constraints:
$$0\le v\le a$$
$$0\le w\le 1$$
$$w_1+\...
- 235
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Stationary conditions for intersection
I wondered about this question for sometime.
Definition of Stationarity
(P)
$\mbox{min} f(x)$
s.t
$x\in C$
Let $f$ be $C^1$ function over a closed and convex set $C$ . then $x^*$ is called a ...
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3
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Find the farthest point in hypercube to an exterior point
Let $\mathcal{U} = \{ [x_1, ..., x_n] \in \mathbb{R}^n : 0 \leq x_i \leq 1\}$ be the unit hypercube and $C \in \mathbb{R}^n\setminus\mathcal{U}$ fixed. Let us consider the following problem
$$ \max_{X ...
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Stationary condition for unit simplex
Consider the minimization problem $$\min_{x \in \Delta_n} f(x)$$ where $f$ is $C^1$ function over the unit simplex $\Delta_n$. Prove that $x^*\in\Delta_n$ is a stationary point of the problem iff ...
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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 ...
- 2,043
2
votes
2
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637
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Pyomo + Ipopt. Speed Issue
I am using Pyomo + Ipopt as solver to solve a NLP problem. The problem is not extremely complex in terms of dimensionality and ...
- 1,409
3
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0
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89
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Appropriate Rotation Matrix in Nonconvex Optimization with Barrier
Let $ x \in \mathbb{R}^n_+$ be a variable such that $\sum_{i=1}^n x_i = 1$. In other words, $x$ is in a probability simplex. I am working on barrier-like functions in nonconvex optimization over such ...
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Cases where RLT/SDP relaxation does not work well with standard quadratic optimization
(For people who don't know what RLT is): I am maximizing an indefinite quadratic function over a standard simplex, i.e., the standard quadratic optimization problem. A well-known approach is to relax ...
- 3,806
4
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0
answers
155
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Fast solvers for LASSO-type non-convex optimization problems
Given $y \in \mathbb{R}^{n \times 1}, X \in \mathbb{R}^{n \times p}$, $p > n$, assume a LASSO-type optimization problem in the form of
$$ \hat\beta=\underset{\beta}{\operatorname{argmin}}\frac{1}{2}...
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votes
1
answer
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Convexity of the variance of a mixture distribution
$X$ is a random variable that is sampled from the mixture of uniform distributions. In other words:
$$X \sim \sum_{i=1}^N w_i \cdot \mathbb{U}(x_i, x_{i+1}),$$
where $\mathbb{U}(x_i, x_{i+1})$ denotes ...
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7
votes
1
answer
123
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Minimizing sum of functions with pairwise dependence
I have formulated a problem where I need to minimize the sum of $N$ functions, with only pairwise dependence between the functions (any single constraint involves only two functions having adjacent ...
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6
votes
1
answer
115
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Does strong duality hold when I dualize only a subset of the constraints?
Suppose I know that for some non-convex program:
\begin{align}\min_x&\quad f(x)\\\text{s.t.}&\quad g_i(x)\leq 0, i \in C\end{align}
strong duality holds for this problem. Now, suppose I form ...
- 329
8
votes
2
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136
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(Iterative?) Solutions to a certain quadratic program with non-convex constraints
Let $y\in\mathbb{R}^m$, $\tau\in\mathbb{R}$ and $X\in\mathbb{R}^{m\times n}$, with $\tau>0$
I would like to efficiently solve the following problem:
Problem 1
Choose $\alpha,z\in\mathbb{R}^m,\beta\...
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2
votes
1
answer
104
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AdaGrad - Sparsity of parameters
I read on Wikipedia:
AdaGrad (for adaptive gradient algorithm) is a modified stochastic gradient descent algorithm with per-parameter learning rate, first published in 2011. Informally, this ...
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votes
2
answers
164
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Local optimum of dual of non-linear program
In general, suppose you have a non-convex optimization problem with constraints and you form the dual problem. If you find a local optimum for the dual problem, will the corresponding primal solution ...
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1
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83
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Maximizing 1-norm: using binary variables to relax non-convexity
It is well-known that when we maximize a 1-norm, e.g., $\|Ax\|_1$, we can use binary variables and obtain a mixed-integer convex problem (otherwise maximizing 1-norm is non-convex). I am mentioning ...
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