1 | # |
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2 | # Couenne options |
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3 | # |
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4 | # Couenne is an open-source solver for nonconvex Mixed-Integer |
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5 | # Nonlinear Programming (MINLP) problems. See more at |
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6 | # |
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7 | # https://projects.coin-or.org/Couenne |
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8 | # |
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9 | # The following is a list of option to tweak the performance of |
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10 | # Couenne. Each option has a brief description and is set to a |
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11 | # default value that we believe works well in most cases. |
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12 | # |
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13 | # |
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14 | # Some of the notation used here is close to that of the paper that |
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15 | # describes Couenne: |
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16 | # |
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17 | # P. Belotti, J. Lee, L. Liberti, F. Margot, A. Waechter, "Branching |
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18 | # and bounds tightening techniques for non-convex MINLP," 2008. |
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19 | # Available on Optimization Online at |
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20 | # |
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21 | # http://www.optimization-online.org/DB_HTML/2008/08/2059.html |
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22 | # |
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23 | # We refer the curious user of Couenne to this paper for more insight |
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24 | # on how the options are used. |
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25 | |
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26 | |
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27 | |
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28 | # |
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29 | # Verbosity/output level options |
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30 | # |
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31 | |
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32 | # display statistics at the end of the run [yes/no]. Set to yes for a |
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33 | # line of output at the end with some brief data on the problem |
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34 | |
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35 | display_stats no |
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36 | |
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37 | |
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38 | # The following are verbosity levels for the main components of Couenne. |
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39 | # Values: 0 for quiet, 11 for excessive output [0..11] |
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40 | |
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41 | branching_print_level 0 # Output level for braching code in Couenne. |
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42 | boundtightening_print_level 0 # Output level for bound tightening code in Couenne |
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43 | convexifying_print_level 0 # Output level for convexifying code in Couenne |
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44 | problem_print_level 7 # Output level for problem manipulation code in Couenne |
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45 | # (4 prints out original problem) |
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46 | nlpheur_print_level 0 # Output level for NLP heuristic in Couenne |
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47 | #disjcuts_print_level 0 # Output level for disjunctive cuts in Couenne - disabled for now |
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48 | |
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49 | orbital_branching yes |
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50 | |
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51 | # |
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52 | # Option for branching rules |
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53 | # |
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54 | |
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55 | # Multipliers of pseudocosts for estimating and update estimation of bound |
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56 | # |
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57 | # When using pseudocosts, the lower bound of a node is estimated by multiplying |
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58 | # the pseudocost by a measure of the "infeasibility" of that variable. |
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59 | # |
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60 | # Valid Settings: |
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61 | # infeasibility (infeasibility returned by object) |
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62 | # projectDist (distance between current LP point and resulting branches' LP points) |
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63 | # interval_lp (width of the interval between bound and current lp point) |
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64 | # interval_lp_rev (similar to interval_lp, reversed) |
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65 | # interval_br (width of the interval between bound and branching point) |
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66 | # interval_br_rev (similar to interval_br, reversed) |
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67 | |
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68 | pseudocost_mult interval_br_rev |
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69 | |
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70 | |
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71 | # Use distance between LP points to update multipliers of |
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72 | # pseudocosts. Can give a better estimate of the change in the node as |
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73 | # a result of the branching rule. |
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74 | |
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75 | pseudocost_mult_lp no |
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76 | |
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77 | |
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78 | # Use Special Ordered Sets (SOS) as indicated in the MINLP |
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79 | # model. Couenne recognizes constraints of the form |
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80 | # |
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81 | # f_1(x) + f_2(x) ... + f_n (x) = 1, |
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82 | # |
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83 | # where f_i (x) are binary expressions, as SOS constraints, and adds |
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84 | # them to the Branch&Bound solver (disabled now -- still testing) |
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85 | |
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86 | enable_sos no |
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87 | |
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88 | |
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89 | # Apply bound tightening before branching |
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90 | # |
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91 | # Upon branching, it may be useful to apply a bound reduction |
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92 | # technique as a preprocessing step for the node, even to check if the |
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93 | # node is feasible |
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94 | |
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95 | branch_fbbt yes |
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96 | |
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97 | |
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98 | # Apply convexification cuts before branching (for now only within |
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99 | # strong branching) |
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100 | # |
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101 | # This is useful to get a more precise lower bound within strong |
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102 | # branching (note: does not work when performing the real branching |
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103 | # rule) |
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104 | |
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105 | branch_conv_cuts yes |
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106 | |
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107 | |
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108 | # Chooses branching point selection strategy |
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109 | # |
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110 | # |
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111 | # When branching on a continuous variable x that has a bound interval |
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112 | # [l,u], the branching point is also important. Couenne implements |
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113 | # several ways of computing the branching point, that may depend on |
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114 | # the current solution of the LP relaxation or on the characteristics |
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115 | # of the linearization that would result from the branching. |
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116 | # |
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117 | # The default value of this option is a convex combination |
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118 | # |
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119 | # alpha xm + (1-alpha) xp |
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120 | # |
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121 | # |
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122 | # where xm is the middle point (l+u)/2, xp is the value of x in the |
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123 | # current LP relaxation, and 0 <= alpha <= 1. Alpha is defined in the |
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124 | # next option. |
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125 | # |
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126 | # Valid Settings: |
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127 | # lp-clamped (LP point clamped in [k,1-k] of the bound intervals (k defined by lp_clamp)) |
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128 | # lp-central (LP point if within [k,1-k] of the bound intervals, |
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129 | # middle point otherwise(k defined by branch_lp_clamp)) |
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130 | # balanced (minimizes max distance from curve to convexification) |
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131 | # min-area (minimizes total area of the two convexifications) |
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132 | # mid-point (convex combination of current point and mid point) |
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133 | # no-branch (do not branch, return null infeasibility; for testing purposes only) |
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134 | |
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135 | branch_pt_select mid-point |
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136 | |
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137 | |
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138 | # Defines convex combination of mid point and current LP point. See |
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139 | # comments on option "branch_pt_select" above. |
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140 | |
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141 | #branch_midpoint_alpha 0.75 |
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142 | |
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143 | |
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144 | # Priority of continuous variable branching |
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145 | # |
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146 | # In Cbc, the Branch&Bound solver on which Couenne is based, integer |
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147 | # variables have a priority of 1000. This parameter is the branching |
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148 | # priority of continuous variables. Setting it to more than 1000 gives |
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149 | # precedence to integer variables, i.e., as long as one integer |
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150 | # variable is currently "infeasible" (i.e. fractional) it will be |
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151 | # branched on. A value below 1000 will give precedence to continuous |
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152 | # variables. |
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153 | |
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154 | #cont_var_priority 2000 |
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155 | |
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156 | |
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157 | # Apply Reduced Cost Branching (instead of the Violation Transfer) -- |
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158 | # MUST have vt_obj enabled |
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159 | # |
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160 | # |
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161 | # Violation Transfer and reduced cost branching are similar techniques |
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162 | # for selecting a branching variable. Couenne implements both and lets |
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163 | # you choose which one to use. Set this to yes to use reduced cost |
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164 | # branching. Experimentally, Violation Transfer appears slightly |
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165 | # better, hence it is preferred by default. |
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166 | |
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167 | red_cost_branching no |
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168 | |
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169 | |
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170 | # Type of branching object for variable selection |
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171 | # |
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172 | # |
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173 | # This parameter determines the branching variable selection |
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174 | # technique. With "vt_obj", the Violation Transfer branching technique |
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175 | # is used. "var_obj" chooses a variable based on the set of nonlinear |
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176 | # expressions that depends on it, while "expr_obj" selects the most |
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177 | # violated nonlinear expression and branches on one of the variables |
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178 | # on which the expression depends. The default is var_obj |
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179 | # |
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180 | # |
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181 | # Valid Settings: |
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182 | # vt_obj use Violation Transfer from Tawarmalani and Sahinidis |
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183 | # var_obj use one object for each variable |
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184 | # expr_obj use one object for each nonlinear expression |
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185 | |
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186 | branching_object var_obj |
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187 | |
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188 | |
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189 | |
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190 | |
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191 | # |
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192 | # Options for bound tightening |
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193 | # |
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194 | |
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195 | |
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196 | # Feasibility-based (cheap) bound tightening (FBBT) |
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197 | # |
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198 | # A fast bound reduction technique. Not very efficient in eliminating |
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199 | # vast portions of the solution set, but recommended. |
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200 | |
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201 | feasibility_bt yes |
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202 | |
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203 | |
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204 | # Optimality-based (expensive) bound tightening (OBBT) |
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205 | # |
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206 | # A slower bound reduction technique that relies on solving 2n LP |
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207 | # problems (n is the number of variables). Probably more efficient |
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208 | # that FBBT, but much more computationally intensive. Recommended |
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209 | # for small problems. See also the next option. |
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210 | |
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211 | optimality_bt yes |
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212 | |
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213 | |
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214 | # Specify the frequency (in terms of nodes) for optimality-based bound |
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215 | # tightening |
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216 | # |
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217 | # As OBBT is expensive, the user may choose to run it only until the |
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218 | # depth k of the branch&bound tree, and with probability exponentially |
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219 | # decreasing with the depth of the branch&bound node at any other node |
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220 | # below depth k. |
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221 | |
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222 | log_num_obbt_per_level 1 |
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223 | |
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224 | |
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225 | # Aggressive feasibility-based bound tightening (to use with NLP points) |
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226 | # |
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227 | # |
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228 | # See the paper for a detailed explanation. This is also an expensive |
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229 | # but efficient way to reduce the solution set |
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230 | |
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231 | aggressive_fbbt yes |
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232 | |
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233 | |
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234 | # Specify the frequency (in terms of nodes) for aggressive bound tightening. |
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235 | # |
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236 | # A parameter analogous to what log_num_obbt_per_level is for OBBT. |
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237 | |
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238 | log_num_abt_per_level 2 |
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239 | |
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240 | |
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241 | # |
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242 | # Options for reformulation and linearization |
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243 | # |
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244 | |
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245 | |
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246 | # Specify the frequency (in terms of nodes) at which couenne ecp cuts |
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247 | # are generated. |
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248 | # |
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249 | # A default value of 1 has linearization inequalities generated at |
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250 | # every node. |
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251 | |
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252 | convexification_cuts 1 |
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253 | |
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254 | |
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255 | # Specify the number of points at which to convexify when |
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256 | # convexification type. |
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257 | |
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258 | convexification_points 4 |
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259 | |
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260 | |
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261 | # Yes if only violated convexification cuts should be added |
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262 | |
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263 | violated_cuts_only yes |
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264 | |
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265 | |
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266 | |
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267 | # |
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268 | # Options for debugging |
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269 | # |
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270 | # Some of these options usually slow down Couenne, and are hence only suggested for debugging purposes. |
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271 | # |
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272 | |
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273 | # Check all LPs through an independent call to |
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274 | # OsiClpSolverInterface::initialSolve() (very expensive) |
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275 | #check_lp no |
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276 | |
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277 | # Artificial cutoff. Used when you know a feasible solution and want |
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278 | # to use it to restrict the solution space |
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279 | |
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280 | #art_cutoff 1e100 |
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281 | |
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282 | # Window around known optimum |
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283 | #opt_window |
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284 | |
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285 | # Artificial lower bound |
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286 | #art_lower -1e100 |
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287 | |
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288 | |
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289 | # |
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290 | # Other options |
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291 | # |
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292 | |
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293 | |
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294 | # Do we search for local solutions of NLP's? |
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295 | local_optimization_heuristic yes |
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296 | |
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297 | |
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298 | # Specify the logarithm of the number of local optimizations to |
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299 | # perform. |
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300 | # |
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301 | # |
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302 | # Analogous to log_num_abt_per_level and log_num_obbt_per_level, this |
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303 | # option determines until which depth of the branch&bound tree the |
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304 | # call to a nonlinear solver is done at every node. Below this depth, |
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305 | # calls to the nonlinear solver happen with probability inversely |
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306 | # proportional to the depth of the node. |
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307 | |
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308 | log_num_local_optimization_per_level 2 |
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309 | |
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310 | |
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311 | # Tolerance for constraints/auxiliary variables |
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312 | # |
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313 | # |
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314 | # A solution is feasible for the original problem if the maximum |
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315 | # violation of a constraint is below this number |
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316 | |
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317 | feas_tolerance 1e-6 |
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318 | |
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319 | |
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320 | # Use quadratic expressions and related exprQuad class |
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321 | # |
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322 | # Allows to use a single operator for quadratic forms (not yet enabled) |
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323 | |
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324 | use_quadratic no |
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