# Case Study: Projection on Polyhedron with Various Norms

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Case study background and problem formulations

Instructions for optimization with PSG Run-File, PSG MATLAB Subroutines and PSG R.

PROBLEM1: problem_cvar_comp_abs

minimizing Cvar_comp_abs (CVaR Absolute Norm)
subject to
Ax ≤b (multiple linear constraints representing convex polyhedron set)
Box constraints (lower bounds on variables)
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Cvar_comp_abs = CVaR component absolute
Box constraints = constraints on individual decision variables
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Alpha =0.8 , Solver Precision = 4
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Data and solution in Run-File Environment

Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 100 100,000 0.10589 2.95
Dataset2 Problem statement Data Solution 100 500,000 0.10834 15.32
Dataset3 Problem statement Data Solution 100 1,000,000 0.11259 29.79
Dataset4 Problem statement Data Solution 100 2,000,000 0.11259 148.05
Dataset5 Problem statement Data Solution 100 5,000,000 0.11370 464.88
Dataset6 Problem statement Data Solution 100 9,000,000 0.11370 829.81
Dataset7 Problem statement Data Solution 500 20,000 0.01783 6.53
Dataset8 Problem statement Data Solution 500 50,000 0.01828 13.13
Dataset9 Problem statement Data Solution 500 100,000 0.01828 32.75
Dataset10 Problem statement Data Solution 500 500,000 0.01856 113.04
Dataset11 Problem statement Data Solution 500 1,000,000 0.01856 303.28
Dataset12 Problem statement Data Solution 500 1,500,000 0.01856 475.0
Dataset13 Problem statement Data Solution 500 1,800,000 0.01856 489.68
Dataset14 Problem statement Data Solution 1000 20,000 0.00866 18.62
Dataset15 Problem statement Data Solution 1000 50,000 0.00867 34.40
Dataset16 Problem statement Data Solution 1000 100,000 0.00877 87.67
Dataset17 Problem statement Data Solution 1000 200,000 0.00878 113.81
Dataset18 Problem statement Data Solution 1000 500,000 0.00878 431.14
Dataset19 Problem statement Data Solution 1000 900,000 0.00878 443.57

NOTE: Problem statements can be simplified using MultiConstraint.

Data and solution in MATLAB Environment

Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 3.50GHz (sec)
Dataset1 Matlab code Data Solution 100 100,000 0.10589 2.85

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Alpha =0.9, Solver Precision = 7
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Data and solution in Run-File Environment

Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 100 100,000 0.10589 4.89
Dataset2 Problem statement Data Solution 100 500,000 0.10833 25.23
Dataset3 Problem statement Data Solution 100 1,000,000 0.11259 41.27
Dataset4 Problem statement Data Solution 100 2,000,000 0.11259 160.24
Dataset5 Problem statement Data Solution 100 5,000,000 0.11370 442.18
Dataset6 Problem statement Data Solution 100 9,000,000 0.11370 847.96

NOTE: Problem statements can be simplified using MultiConstraint.

Data and solution in MATLAB Environment

Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 3.50GHz (sec)
Dataset1 Matlab code Data Solution 100 100,000 0.10589 4.79
Dataset2 Matlab code Data Solution 100 500,000 0.10833 24.73
Dataset3 Matlab code Data Solution 100 1,000,000 0.11259 40.14

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Alpha =0.955279, Solver Precision = 7
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Data and solution in Run-File Environment

Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 500 20,000 0.01783 21.83
Dataset2 Problem statement Data Solution 500 50,000 0.01828 40.46
Dataset3 Problem statement Data Solution 500 100,000 0.01828 94.30
Dataset4 Problem statement Data Solution 500 500,000 0.01855 717.9
Dataset5 Problem statement Data Solution 500 1,000,000 0.01855 1295.71
Dataset6 Problem statement Data Solution 500 1,500,000 0.01855 1739.97
Dataset7 Problem statement Data Solution 500 1,800,000 0.01855 1643.09

NOTE: Problem statements can be simplified using MultiConstraint.

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Alpha =0.968377, Solver Precision = 7
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Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 1000 20,000 0.00866 92.55
Dataset2 Problem statement Data Solution 1000 50,000 0.00867 273.03
Dataset3 Problem statement Data Solution 1000 100,000 0.00877 408.29
Dataset4 Problem statement Data Solution 1000 200,000 0.00877 1363.49
Dataset5 Problem statement Data Solution 1000 500,000 0.00877 2617.07
Dataset6 Problem statement Data Solution 1000 900,000 0.00877 4654.56

NOTE: Problem statements can be simplified using MultiConstraint.

PROBLEM2: problem_mix_L1_L_Infinity

minimizing [(1-lambda)*polynom_abs+ lambda*max_comp_abs]
subject to
Ax ≤b (multiple linear constraints representing convex polyhedron set)
Box constraints (lower bounds on variables)
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polynom_abs = polynomial absolute function
max_comp_abs=maximum component absolute function
Box constraints = constraints on individual decision variables
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Lambda =0.90909, Solver Precision = 7
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Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 100 100,000 1.01459 6.62
Dataset2 Problem statement Data Solution 100 500,000 1.05439 27.53
Dataset3 Problem statement Data Solution 100 1,000,000 1.06657 47.81
Dataset4 Problem statement Data Solution 100 2,000,000 1.07531 168.45
Dataset5 Problem statement Data Solution 100 5,000,000 1.09822 468.64
Dataset6 Problem statement Data Solution 100 9,000,000 1.10039 851.35

NOTE: Problem statements can be simplified using MultiConstraint.

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Lambda =0.95744, Solver Precision = 7
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Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 500 20,000 0.38609 35.07
Dataset2 Problem statement Data Solution 500 50,000 0.38966 70.52
Dataset3 Problem statement Data Solution 500 100,000 0.39170 128.08
Dataset4 Problem statement Data Solution 500 500,000 0.39654 744.98
Dataset5 Problem statement Data Solution 500 1,000,000 0.39868 1571.72
Dataset6 Problem statement Data Solution 500 1,500,000 0.39956 2574.43
Dataset7 Problem statement Data Solution 500 1,800,000 0.40005 2909.3

NOTE: Problem statements can be simplified using MultiConstraint.

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Lambda =0.96923, Solver Precision = 7
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Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 1000 20,000 0.26782 161.54
Dataset2 Problem statement Data Solution 1000 50,000 0.26938 318.55
Dataset3 Problem statement Data Solution 1000 100,000 0.27064 475.09
Dataset4 Problem statement Data Solution 1000 200,000 0.27165 1209.68
Dataset5 Problem statement Data Solution 1000 500,000 0.27194 3165.31
Dataset6 Problem statement Data Solution 1000 900,000 0.27194 5612.34

NOTE: Problem statements can be simplified using MultiConstraint.

minimizing quadratic (square of L_2 norm)
subject to
Ax ≤b (multiple linear constraints representing convex polyhedron set)
Box constraints (lower bounds on variables)
——————————————————————–——————
Box constraints = constraints on individual decision variables
——————————————————————–——————

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Solver Precision = 7
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Problem Datasets # of Variables # of Rows Objective Value Solving Time, PC 2.83GHz (sec)
Dataset1 Problem statement Data Solution 100 100,000 1.03483 2.47
Dataset2 Problem statement Data Solution 100 500,000 1.11188 15.83
Dataset3 Problem statement Data Solution 100 1,000,000 1.14674 30.70
Dataset4 Problem statement Data Solution 100 2,000,000 1.15551 134.57
Dataset5 Problem statement Data Solution 100 5,000,000 1.20912 324.14
Dataset6 Problem statement Data Solution 100 9,000,000 1.21178 914.29
Dataset7 Problem statement Data Solution 500 20,000 0.15307 4.30
Dataset8 Problem statement Data Solution 500 50,000 0.15612 21.83
Dataset9 Problem statement Data Solution 500 100,000 0.15752 25.26
Dataset10 Problem statement Data Solution 500 500,000 0.16135 115.8
Dataset11 Problem statement Data Solution 500 1,000,000 0.16346 291.92
Dataset12 Problem statement Data Solution 500 1,500,000 0.16393 335.87
Dataset13 Problem statement Data Solution 500 1,800,000 0.16425 388.55
Dataset14 Problem statement Data Solution 1000 20,000 0.05064 0.63
Dataset15 Problem statement Data Solution 1000 50,000 0.05064 0.94
Dataset16 Problem statement Data Solution 1000 100,000 0.05064 1.50
Dataset17 Problem statement Data Solution 1000 200,000 0.05064 2.57
Dataset18 Problem statement Data Solution 1000 500,000 0.05064 5.87
Dataset19 Problem statement Data Solution 1000 900,000 0.05064 162.24

NOTE: Problem statements can be simplified using MultiConstraint.

CASE STUDY SUMMARY

This case study considers the projection problems with various norms on a polyhedron set given by a system of linear inequalities. In particular, we consider the Scaled CVaR Absolute Norm introduced in Pavlikov and Uryasev (2013). The Scaled CVaR Absolute Norm in is a family of norms based on CVaR concept. This family has a control parameter <imgsrc=”http: 129.49.108.249=”” wp-content=”” uploads=”” 2013=”” 05=”” cs_projection_cvar_norm_cvar_comp_abs_clip_image009.gif”=”” alt=”” width=”15″ height=”14″>, so-called confidence level, controlling conservativeness of the norm. CVaR term and the optimization approach for CVaR was introduced in Rockafellar and Uryasev (2000). For the most conservative case, , the Scaled CVaR Absolute Norm equals the maximum absolute value of components of the vector. The least conservative norm, with , averages absolute values of all components. In the intermediate case, for , when is a multiple of , the norm of a vector is defined as the average of largest absolute values of components. If is not a multiple of , then Scaled CVaR Absolute Norm is defined as a convex combination of two values of the norm with nearest to upper and lower multiples of confidence levels. Computational experiments for and spaces are presented. Several polyhedron sets with different number of hyperplanes were generated, such that . The projection of 0 on is solved with CVaR Absolute Norm, norm, and the weighted average of and norms. Problem 1 solves the projection problem with CVaR Absolute Norm with confidence level for different dimensions and different polyhedrons.
Then, Problem 1 solves the projection problems with CVaR Absolute Norm for dimensions = 100, 500, 1000,and confidence levels, , , , accordingly. The confidence levels, in this case, were selected with the formula , which corresponds to the best approximation of norm by the CVaR Absolute Norm, see Gotoh and Uryasev (2013).
Problem 2 solves projection problems with weighted average of and norms for dimensions = 100, 500, 1000. Weighting is done with the parameter as follows: The weighting parameter is a function of dimension to make the best approximation of the norm by the weighted average of the and norms, i.e., , , , see Gotoh and Uryasev (2013).
Problem 3 solves projection problems with square of norm for dimensions = 100, 500, 1000.</imgsrc=”http:>

References

• Pavlikov K, and S. Uryasev (2013): CVaR Absolute Norm and Applications in Optimization.
Research Report # 2013-1.
• Rockafellar, R.T. and S. Uryasev (2013): The Fundamental Risk Quadrangle in Risk
Management, Optimization, and Statistical Estimation. Surveys in Operations Research and
Management Science, 18 (to appear).
• Rockafellar, R. T. and Uryasev, S. (2000), “Optimization of conditional value-at-risk”,
Journal of Risk , Vol. 2, pp. 21–41.
• Gotoh, J. and S. Uryasev (2013): Approximation of Euclidean norm by LP-representable
norms and applications. Draft paper.