XSLP_MIPALGORITHM, SLPMIPALGORITHM

Bitmap describing the MISLP algorithms to be used

Integer

Bit	Meaning
0	Solve initial SLP to convergence.
2	Relax step bounds according to XSLP_MIPRELAXSTEPBOUNDS after initial node.
3	Fix step bounds according to XSLP_MIPFIXSTEPBOUNDS after initial node.
4	Relax step bounds according to XSLP_MIPRELAXSTEPBOUNDS at each node.
5	Fix step bounds according to XSLP_MIPFIXSTEPBOUNDS at each node.
6	Limit iterations at each node to XSLP_MIPITERLIMIT.
7	Relax step bounds according to XSLP_MIPRELAXSTEPBOUNDS after MIP solution is found.
8	Fix step bounds according to XSLP_MIPFIXSTEPBOUNDS after MIP solution is found.
9	Use MIP at each SLP iteration instead of SLP at each node.
10	Use MIP on converged SLP solution and then SLP on the resulting MIP solution.

17 (bits 0 and4 are set)

XSLP_MIPALGORITHM determines the strategy of XSLPnlpoptimize for solving MINLP problems. The recommended approach is to solve the problem first without reference to the discrete variables. This can be handled automatically by setting bit 0 of XSLP_MIPALGORITHM; if done manually, then optimize using the "l" option to prevent the Optimizer presolve from changing the problem.
Some versions of the optimizer re-run the initial node as part of the tree search; it is possible to initiate a new SLP optimization at this point by relaxing or fixing step bounds (use bits 2 and 3). If step bounds are fixed for a class of variable, then the variables in that class will not change their value in any child node.
At each node, it is possible to relax or fix step bounds. It is recommended that step bounds are relaxed, so that the new problem can be solved starting from its parent, but without undue restrictions cased by step bounding (use bit 4). Exceptionally, it may be preferable to restrict the freedom of child nodes by relaxing fewer types of step bound or fixing the values of some classes of variable (use bit 5).
When the optimal node has been found, it is possible to fix the discrete variables and then re-optimize with SLP. Step bounds can be relaxed or fixed for this optimization as well (use bits 7 and 8).
Although it is ultimately necessary to solve the optimal node to convergence, individual nodes can be truncated after XSLP_MIPITERLIMIT SLP iterations. Set bit 6 to activate this feature.
The normal MISLP algorithm uses SLP at each node. One alternative strategy is to use the MIP optimizer for solving each SLP iteration. Set bit 9 to implement this strategy ("MIP within SLP").
Another strategy is to solve the problem to convergence ignoring the nature of the integer variables. Then, fixing the linearization, use MIP to find the optimal setting of the discrete variables. Then, fixing the discrete variables, but varying the linearization, solve to convergence. Set bit 10 to implement this strategy ("SLP then MIP").
For mode details about MISLP algorithms and strategies, see the separate section.

The following constants are provided for setting these bits:

Setting bit 0	XSLP_MIPINITIALSLP
Setting bit 2	XSLP_MIPINITIALRELAXSLP
Setting bit 3	XSLP_MIPINITIALFIXSLP
Setting bit 4	XSLP_MIPNODERELAXSLP
Setting bit 5	XSLP_MIPNODEFIXSLP
Setting bit 6	XSLP_MIPNODELIMITSLP
Setting bit 7	XSLP_MIPFINALRELAXSLP
Setting bit 8	XSLP_MIPFINALFIXSLP
Setting bit 9	XSLP_MIPWITHINSLP
Setting bit 10	XSLP_SLPTHENMIP
Setting bit 11	XSLP_NOFINALROUNDING

XSLPnlpoptimize

XSLP_ALGORITHM, XSLP_MIPFIXSTEPBOUNDS, XSLP_MIPITERLIMIT, XSLP_MIPRELAXSTEPBOUNDS