Questions tagged [lagrange-multipliers]

For questions related to Lagrange (or Lagrangian) multipliers, coefficients used to penalize violations of constraints that have been relaxed from an optimization problem.

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Convex Optimization Problem with norm inequality constraint

Consider the following optimization problem: \begin{align} \inf_{x,y}&\quad(x-x_0)^\top A(x-x_0) + (y-y_0)^\top B(y-y_0) \\\text{s.t.}&\quad x^\top a\geq0,\\ & \quad y^\top b\geq0, \\& ...
1
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1answer
98 views

How to find the optimal solution of a convex program given all KKT points?

Suppose we have a parametric convex program with some constraints. \begin{equation} \begin{split} \max_{x} \: & f(x,\mathbf{a})\\ & g_1(x,\mathbf{a})\le 0 \\ & g_2(x,\mathbf{a}) \le 0 \end{...
2
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0answers
88 views

Geometric interpretation of KKT conditions

I can explain why Lagrange multipliers work for scalar functions by vector calculus. Consider optimizing $f(\vec{x})$ subjected to the constraint $g(\vec{x}) = c$. At the optima, we can move ...
2
votes
1answer
94 views

KKT conditions analysis for binary constraints

I am wondering if boolean constraints in a linear program can be solved (after linear relaxation from $x\in\{0,1\}$ to both $x\ge0$ and $x\le1$) using KKT analysis. Most of the algorithms that I have ...
3
votes
1answer
119 views

Linear Relaxation of Boolean Constraint for Solving Integer Linear Program Using KKT

I am trying to convert a boolean LP to LP using LP relaxation by converting $x \in {0,1}$ to both $x \ge 0$ and $x \le 1$. Then to use it in my problem analysis, I am trying to build the KKT ...
3
votes
1answer
102 views

Method of Multipliers: Why is the next iterate always dual feasible?

I am reading this expository paper on ADMM by Boyd, et. al. Consider the problem \begin{align*} &\min f(x)\\ & \ \text{s.t.} \ \ \ Ax = b \end{align*} with Lagrangian $L(x, \lambda) = f(x) + \...
2
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1answer
99 views

Recovering Primal Solution from Dual solution

Consider the problem \begin{align*} &\min f(x)\\ & \ \text{s.t.} \ \ \ Ax = b \end{align*} In this expository paper, Boyd claims (top of page $8$) that if: $\lambda^*$ is a dual optimal ...
7
votes
0answers
100 views

Estimate lagrangian multiplier based on instance characteristics

Assume we have a simple resource allocation problem, where all players have the same cost, but a different utility $a_s$. The resources assigned to a certain player must be between $L$ and $M$. ...
3
votes
1answer
217 views

Why is the Lagrange Multiplier not equal the Shadow Price (Excel solver, Matlab linprog, Gurobi)?

I have a LP with equality and inequality constraints. When solving the LP with the excel-solver (GRG Nonlinear) the sensitivity report returns the lagrange multiplier for all constraints. When solving ...
1
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0answers
64 views

How to solve this problem by Lagrange duality?

This is a convex problem and although it can be well solved by CVX, I want to know how it can be solved by the Lagrange duality method. The derivations with regard to $L_k$ and $B_k$ are constants, ...
5
votes
1answer
590 views

Simplex Multiplier

I am reading through a book which provides an example of a linear program given by \begin{align}\min&\quad-24y_{1}-28y_{2}\\\text{s.t.}&\quad6y_{1}+10y_{2} \leq 2400\\&\quad8y_{1}+5y_{2} \...
2
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0answers
63 views

Deriving KKT Conditions for time-step equations

I have a variable $e(h)$, and below is the part of the Lagrangian equation where I am taking the derivative with respect to $e(h)$. $$\frac{\partial }{\partial e(h)} \hspace{.2cm}\mu_1(h)(e(h)-\bar{E}...
1
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1answer
95 views

Simple nonlinear programming using convexity analysis and KKT

I want to solve the following two-variate nonlinear programming using KKT conditions: $$ \begin{align} \begin{split} \max \quad & 15 \sqrt{x_{1}} + 16 \sqrt{x_{2}} \\ \text{s.t.} \quad &...
11
votes
3answers
220 views

Applicability of Lagrange Multipliers in the analysis of large-scale MILPs?

Qualitatively, in my experience in the solving of large scale MILPs, it is common that binary variables corresponding to "edge possibility" components are frequently chosen. Intuitively, these seem ...