Questions tagged [yalmip]
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9 questions
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Approximate Solutions and Solver Settings in CPLEX and Gurobi
CPLEX offers functionalities to approximate solutions for nonconvex optimization problems rather than guaranteeing global optimality. It includes an option to find solutions satisfying first-order ...
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Global optimizers handling minimization of an expression arising from the likelihood of a multivariate normal
I am interested in converting the following optimisation problem into a form that an exponential cone and/or SDP solver such as MOSEK can handle. This is a multivariate version of the question I ...
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Global optimizers handling minimization of expressions like $\log{v}+\frac{1}{v}$
Consider the simple problem of maximum likelihood estimation of the variance of a mean zero normal distribution. The expression to be minimised is:
$$N \log{v}+\frac{1}{v}\sum_{n=1}^N{b_n^2},$$
where $...
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How do I pass an objective bound to Gurobi?
I have a non-convex Quadratic Programming over unite simplex set. I have a valid lower bound on the objective function (goal is minimization problem).
If I add a constraint like
$$f(x)\geq lower~bound,...
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Does YALMIP allow a user-defined function for the objective function and constraints?
I have a robust optimization problem where the decision variable is a matrix, and the uncertain parameter is a vector. My matrix is L, and the uncertain parameter ...
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How to normalize the objective functions of multi-objective optimization for a MPC?
I have a MPC with two objective functions, one that minimises fuel consumption and one that minimises the travel time of a vessel. I want to combine these two objectives into one weighted objective, ...
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Benders decompositions: Number of iterations does not remain the same
I am solving an LP (i.e 118-bus system economic dispatch for 130% loading) using Benders decomposition. The problem takes 26 iterations to converge. This means that the process adds 25 cuts to the ...
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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 ...
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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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