Examples of using Linear constraints in English and their translations into Portuguese
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Even the hint of effort orintention to lighter keeps the mind in its dualistic and linear constraints.
Present a technique for solving optimization problems under linear constraints involving only two technical variables.
Use of stabilizers sets as linear constraints to obtain the gains of a PID controller obtained via quadratic optimization, BE.EP. MS.
Network DEA is composed of a family of DEA models that establishes linear constraints for each one of the analyzed dimensions.
Current techniques either handle only linear constraints or only support continuous distributions using a¿discretization¿of the input domain, leading to imprecise and costly results.
Function: minimize_lp(obj, cond,[pos]) Minimizes a linear objective function obj subject to some linear constraints cond.
For a better understanding about linear constraints and feasible solutions, we will define convex set, polyhedron and polytope.
Function: maximize_lp(obj, cond,[pos])Maximizes linear objective function obj subject to some linear constraints cond.
The simplex method,which runs through the vertices of the polyhedron determined by linear constraints, will be presented along with the method of the two phases and some examples.
Refined procedures for solving and performing sensitivity analysis on uni and multi dimensional, local or global optimization problems which may ormay not have linear constraints.
Minimum standards for accuracy:( a) all linear constraints must be fulfilled where applicable e.g. assets and liabilities must balance, subtotals must add up to totals;
EJB Suite offering refined procedures for solving and performing sensitivity analysis on uni and multi dimensional, local or global optimization problems which may ormay not have linear constraints.
Accordingly, all the linear constraints between the BSIs must be fulfilled balance sheets must balance, subtotals must add up to totals and they should not exceed the value of the total series.
Minimum standards for accuracy( e)the statistical information must be correct:- all linear constraints must be fulfilled( e.g. balance sheets must balance, sub-totals must add up to totals);
To illustrate, a network instance with 70 nodes, 1035 edges and 1 message being routed during a 1 hour and30 minute period produces a formulation with 11.5 million linear constraints and 11 million variables.
Minimum standards for accuracy( e) The statistical information must be correct:--- all linear constraints must be fulfilled( e.g. subtotals must add up to totals);--- data must be consistent across frequencies;
To control the deficit risk is proposed the use of the risk metric known as cvar(conditional value at risk), because it is characterized as a coherent risk metric, andcan be implemented through a set of linear constraints.
Minimum standards for accuracy( e)The statistical information shall be correct: all linear constraints shall be fulfilled( e.g. balance sheets must balance, subtotals must add up to totals); and data shall be consistent across all frequencies;
This thesis proposes a family with three linearly constrained with'1-norm adaptive algorithms. the algorithm that gives rise to this family is the'1-norm linearly- constrained least-mean-square.the algorithms proposed by this work have as distinguishing feature the flexibility resulting from the coexistence between'1-norm and linear constraints.
Minimum standards for accuracy( e) The statistical information must be correct:--- all linear constraints must be fulfilled( e.g. balance sheets must balance, sub-totals must add up to totals), and--- data must be consistent across all frequencies;
Sanctions may be imposed following an infringement procedure in the event of failure to comply with minimum standards for transmission( in relation to timeliness and technical reporting requirements),accuracy( in relation to linear constraints and data consistency across frequencies) and conceptual compliance in relation to definitions and classifi cations.
Minimum standards for accuracy:( a) the statistical information must be correct:--- all linear constraints must be fulfilled( e.g. assets and liabilities must balance, subtotals must add up to totals), and data must be consistent across all frequencies;---( b) c.
An extension that has gained significant popularity since 2003is satisfiability modulo theories(SMT) that can enrich CNF formulas with linear constraints, arrays, all-different constraints, uninterpreted functions, etc. Such extensions typically remain NP-complete, but very efficient solvers are now available that can handle many such kinds of constraints. .
In this method, the linear programming problems are solved bythe simplex primal method; in the problems with nonlinear objective function and linear constraints is used the reduced gradient method; and for solving problems with nonlinear objective function and nonlinear constraints: a first-order taylor s linearization in the nonlinear constraints, an augmented lagrangian function and the reduced gradient method are used.
If the objective function is quadratic and the constraints are linear, quadratic programming techniques are used.
The proposed procedure for generating individuals andmaintaining the feasibility of the population is straightforward when linear equality constraints are considered, but req.
Thinking the curriculum from a rhizome perspective enables us to build new connections that allow us to get rid of linear relationships constraints and, even if shortly, to produce multiplicities.
It can solve constraints over finite domains of integers and also linear rational constraints.
The minimum cost flow problem can be solved by linear programming,since we optimize a linear function, and all constraints are linear.