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3  Problem Types and Solver Routines

Section 3.1 defines all problem types in TOMLAB . Each problem definition is accompanied by brief suggestions on suitable solvers. This is followed in Section 3.2 by a complete list of the available solver routines in TOMLAB  and the various available extensions, such as /SOL and /CGO.

3.1  Problem Types Defined in TOMLAB

The unconstrained optimization (uc) problem is defined as
 
min
x
f(x)
   
s/t
xL ≤ x ≤ xU,
    (1)
where x, xL, xU ∈ Rn and f(x) ∈ R. For unbounded variables, the corresponding elements of xL,xU may be set to ± ∞.

Recommended solvers: TOMLAB /KNITRO and TOMLAB /SNOPT.

The TOMLAB  Base Module routine ucSolve  includes several of the most popular search step methods for unconstrained optimization. Bound constraints are treated as described in Gill et. al. [31]. The search step methods for unconstrained optimization included in ucSolve  are: the Newton method, the quasi-Newton BFGS and DFP method, the Fletcher-Reeves and Polak-Ribiere conjugate-gradient method, and the Fletcher conjugate descent method. The quasi-Newton methods may either update the inverse Hessian (standard) or the Hessian itself. The Newton method and the quasi-Newton methods updating the Hessian use a subspace minimization technique to handle rank problems, see Lindström [56]. The quasi-Newton algorithms also use safe guarding techniques to avoid rank problem in the updated matrix.

Another TOMLAB  Base Module solver suitable for unconstrained problems is the structural trust region algorithm sTrustr , combined with an initial trust region radius algorithm. The code is based on the algorithms in [15] and [70], and treats partially separable functions. Safeguarded BFGS or DFP are used for the quasi-Newton update, if the analytical Hessian is not used. The set of constrained nonlinear solvers could also be used for unconstrained problems.

The quadratic programming (qp) problem is defined as
 
min
x
f(x) =
1
2
xT F x + cT x
   
s/t
xL ≤ x ≤ xU,
bL ≤ A x ≤ bU
    (2)
where c, x, xL, xU ∈ Rn, F ∈ Rn × n, A ∈ Rm1 × n, and bL,bU ∈ Rm1.

Recommended solvers: TOMLAB /KNITRO, TOMLAB /SNOPT and TOMLAB /MINLP.

A positive semidefinite F-matrix gives a convex QP, otherwise the problem is nonconvex. Nonconvex quadratic programs are solved with a standard active-set method [57], implemented in the TOM routine qpSolve . qpSolve  explicitly treats both inequality and equality constraints, as well as lower and upper bounds on the variables (simple bounds). It converges to a local minimum for indefinite quadratic programs.

In TOMLAB  MINOS  in the general or the QP-version (QP-MINOS ), or the dense QP solver QPOPT  may be used. In the TOMLAB  /SOL extension the SQOPT  solver is suitable for both dense and large, sparse convex QP and SNOPT  works fine for dense or sparse nonconvex QP.

For very large-scale problems, an interior point solver is recommended, such as TOMLAB /KNITRO or TOMLAB /BARNLP.

TOMLAB /CPLEX, TOMLAB /Xpress and TOMLAB /XA should always be considered for large-scale QP problems.

The constrained nonlinear optimization problem (con) is defined as
 
min
x
f(x)
   
s/t
xL ≤ x ≤ xU,
bL ≤ A x ≤ bU
cL ≤ c(x) ≤ cU
    (3)
where x, xL, xU ∈ Rn, f(x) ∈ R, A ∈ Rm1 × n, bL,bU ∈ Rm1 and cL,c(x),cU ∈ Rm2.

Recommended solvers: TOMLAB /SNOPT, TOMLAB /NPSOL and TOMLAB /KNITRO.

For general constrained nonlinear optimization a sequential quadratic programming (SQP) method by Schittkowski [72] is implemented in the TOMLAB  Base Module solver conSolve . conSolve  also includes an implementation of the Han-Powell SQP method. There are also a TOMLAB  Base Module routine nlpSolve  implementing the Filter SQP by Fletcher and Leyffer presented in [24].

Another constrained solver in TOMLAB  is the structural trust region algorithm sTrustr , described above. Currently, sTrustr  only solves problems where the feasible region, defined by the constraints, is convex. TOMLAB /MINOS solves constrained nonlinear programs. The TOMLAB  /SOL extension gives an additional set of general solvers for dense or sparse problems.

sTrustr , pdco  and pdsco  in TOMLAB Base Module  handle nonlinear problems with linear constraints only.

There are many other options for large-scale nonlinear optimization to consider in TOMLAB . TOMLAB /CONOPT, TOMLAB /BARNLP, TOMLAB /MINLP, TOMLAB /NLPQL and TOMLAB /SPRNLP.

The box-bounded global optimization (glb) problem is defined as
 
min
x
f(x)
   
s/t
−∞ < xL ≤ x ≤ xU < ∞ ,
    (4)
where x, xL, xU ∈ Rn, f(x) ∈ R, i.e. problems of the form (1) that have finite simple bounds on all variables.

Recommended solvers: TOMLAB /LGO and TOMLAB /CGO with TOMLAB /SOL.

The TOM solver glbSolve  implements the DIRECT algorithm [16], which is a modification of the standard Lipschitzian approach that eliminates the need to specify a Lipschitz constant. In glbSolve  no derivative information is used. For global optimization problems with expensive function evaluations the TOMLAB  /CGO routine ego  implements the Efficient Global Optimization (EGO) algorithm [18]. The idea of the EGO algorithm is to first fit a response surface to data collected by evaluating the objective function at a few points. Then, EGO balances between finding the minimum of the surface and improving the approximation by sampling where the prediction error may be high.

The global mixed-integer nonlinear programming (glc) problem is defined as
 
min
x
f(x)
   
s/t
−∞ < xL ≤ x ≤ xU < ∞
  bL ≤ A x ≤ bU  
  cL ≤ c(x) ≤ cU,  xj ∈ N   ∀ j ∈ I,
    (5)
where x, xL, xU ∈ Rn, f(x) ∈ R, A ∈ Rm1 × n, bL,bU ∈ Rm1 and cL,c(x),cU ∈ Rm2. The variables x ∈ I, the index subset of 1,...,n, are restricted to be integers.

Recommended solvers: TOMLAB /OQNLP.

The TOMLAB  Base Module solver glcSolve  implements an extended version of the DIRECT algorithm [55], that handles problems with both nonlinear and integer constraints.

The linear programming (lp) problem is defined as
 
min
x
f(x) = cT x
   
s/t
xL ≤ x ≤ xU,
bL ≤ A x ≤ bU
    (6)
where c, x, xL, xU ∈ Rn, A ∈ Rm1 × n, and bL,bU ∈ Rm1.

Recommended solvers: TOMLAB /CPLEX, TOMLAB /Xpress and TOMLAB /XA.

The TOMLAB  Base Module solver lpSimplex  implements a simplex algorithm for lp problems.

When a dual feasible point is known in (6) it is efficient to use the dual simplex algorithm implemented in the TOMLAB  Base Module solver DualSolve . In TOMLAB /MINOS the LP interface to MINOS , called LP-MINOS  is efficient for solving large, sparse LP problems. Dense problems are solved by LPOPT . The TOMLAB  /SOL extension gives the additional possibility of using SQOPT  to solve large, sparse LP.

The recommended solvers normally outperforms all other solvers.

The mixed-integer programming problem (mip) is defined as
 
min
x
f(x) = cT x
   
s/t
xL ≤ x ≤ xU,
bL ≤ A x ≤ bU,  xj ∈ N   ∀ j ∈ I
    (7)
where c, x, xL, xU ∈ Rn, A ∈ Rm1 × n, and bL,bU ∈ Rm1. The variables x ∈ I, the index subset of 1,...,n are restricted to be integers. Equality constraints are defined by setting the lower bound equal to the upper bound, i.e. for constraint i: bL(i) = bU(i).

Recommended solvers: TOMLAB /CPLEX, TOMLAB /Xpress and TOMLAB /XA.

Mixed-integer programs can be solved using the TOMLAB  Base Module routine mipSolve  that implements a standard branch-and-bound algorithm, see Nemhauser and Wolsey [61, chap. 8]. When dual feasible solutions are available, mipSolve is using the TOMLAB  dual simplex solver DualSolve to solve subproblems, which is significantly faster than using an ordinary linear programming solver, like the TOMLAB  lpSimplex. mipSolve  also implements user defined priorities for variable selection, and different tree search strategies. For 0/1 - knapsack problems a round-down primal heuristic is included. Another TOMLAB  Base Module solver is the cutting plane routine cutplane , using Gomory cuts. It is recommended to use mipSolve  with the LP version of MINOS  with warm starts for the subproblems, giving great speed improvement. The TOMLAB /Xpress extension gives access to the state-of-the-art LP, QP, MILP and MIQP solver Xpress-MP. For many MIP problems, it is necessary to use such a powerful solver, if the solution should be obtained in any reasonable time frame. TOMLAB /CPLEX is equally powerful as TOMLAB /Xpress and also includes a network solver.

The linear least squares (lls) problem is defined as
 
min
x
f(x) =
1
2
|| C x − d ||
   
s/t
xL ≤ x ≤ xU,
bL ≤ A x ≤ bU
    (8)
where x, xL, xU ∈ Rn, d ∈ RM, C ∈ RM × n, A ∈ Rm1 × n, bL,bU ∈ Rm1 and cL,c(x),cU ∈ Rm2.

Recommended solvers: TOMLAB /LSSOL.

Tlsqr  solves unconstrained sparse lls problems. lsei  solves the general dense problems. Tnnls  is a fast and robust replacement for the Matlab  nnls. The general least squares solver clsSolve  may also be used. In the TOMLAB  /NPSOL or TOMLAB  /SOL extension the LSSOL  solver is suitable for dense linear least squares problems.

The constrained nonlinear least squares (cls) problem is defined as
 
min
x
f(x) =
1
2
r(x)T r(x)
   
s/t
xL ≤ x ≤ xU,
bL ≤ A x ≤ bU
cL ≤ c(x) ≤ cU
    (9)
where x, xL, xU ∈ Rn, r(x) ∈ RM, A ∈ Rm1 × n, bL,bU ∈ Rm1 and cL,c(x),cU ∈ Rm2.

Recommended solvers: TOMLAB /NLSSOL.

The TOMLAB  Base Module nonlinear least squares solver clsSolve  treats problems with bound constraints in a similar way as the routine ucSolve . This strategy is combined with an active-set strategy to handle linear equality and inequality constraints [7].

clsSolve  includes four optimization methods for nonlinear least squares problems: the Gauss-Newton method, the Al-Baali-Fletcher [2] and the Fletcher-Xu [22] hybrid method, and the Huschens TSSM method [53]. If rank problems occur, the solver uses subspace minimization. The line search algorithm used is the same as for unconstrained problems.

Another fast and robust solver is NLSSOL , available in the TOMLAB  /NPSOL or the TOMLAB  /SOL extension toolbox.

One important utility routine is the TOMLAB  Base Module line search algorithm LineSearch , used by the solvers conSolve , clsSolve  and ucSolve . It implements a modified version of an algorithm by Fletcher [23, chap. 2]. The line search algorithm uses quadratic and cubic interpolation, see Section 12.9.

Another TOMLAB  Base Module routine, preSolve , is running a presolve analysis on a system of linear equalities, linear inequalities and simple bounds. An algorithm by Gondzio [39], somewhat modified, is implemented in preSolve . See [7] for a more detailed presentation.

The linear semi-definite programming problem with linear matrix inequalities (sdp) is defined as

 
min
x
f(x) = cTx
   
s/t xL ≤ x ≤ xU
  bL ≤ Ax ≤ bU
 
Q0i +
 
Σ
k=1
n Qkixk T 0,     i=1,…,m.
    (10)
where c, x, xL, xU ∈ Rn, A ∈ Rml × n, bL,bU ∈ Rml and Qki are symmetric matrices of similar dimensions in each constraint i. If there are several LMI constraints, each may have it's own dimension.

Recommended solvers: TOMLAB /PENSDP and TOMLAB /PENBMI.

The linear semi-definite programming problem with bilinear matrix inequalities (bmi) is defined similarly to but with the matrix inequality

Q0i +
 
Σ
k=1
n Qkixk +
 
Σ
k=1
n
 
Σ
l=1
n xkxlKkli T 0    (11)


The MEX solvers pensdp  and penbmi  treat sdp and bmi problems, respectively. These are available in the TOMLAB /PENSDP  and TOMLAB  /PENBMI toolboxes.

The MEX-file solver pensdp  available in the TOMLAB /PENSDP  toolbox implements a penalty method aimed at large-scale dense and sparse sdp problems. The interface to the solver allows for data input in the sparse SDPA input format as well as a TOMLAB  specific format corresponding to.

The MEX-file solver penbmi  available in the TOMLAB /PENBMI  toolbox is similar to pensdp , with added support for the bilinear matrix inequalities.

3.2  Solver Routines in TOMLAB

3.2.1  TOMLAB Base Module

TOMLAB  includes a large set of optimization solvers. Most of them were originally developed by the Applied Optimization and Modeling group (TOM) [45]. Since then they have been improved e.g. to handle Matlab  sparse arrays and been further developed. Table 3.2.1 lists the main set of TOM optimization solvers in all versions of TOMLAB .
Function Description Section
clsSolve  Constrained nonlinear least squares. Handles simple bounds and linear equality and inequality constraints using an active-set strategy. Implements Gauss-Newton, and hybrid quasi-Newton and Gauss-Newton methods. 11.1.1  
conSolve  Constrained nonlinear minimization solver using two different sequential quadratic programming methods. 11.1.2  
cutplane  Mixed-integer programming using a cutting plane algorithm. 11.1.3
DualSolve  Solves a linear program with a dual feasible starting point. 11.1.4
expSolve  Solves exponential fitting problems. 11.1.5
glbFast  Box-bounded global optimization, using only function values. Fortran MEX implementation of glbSolve . 11.1.7
glbSolve  Box-bounded global optimization, using only function values. 11.1.8
glcCluster  Hybrid algorithm for constrained mixed-integer global optimization. Uses a combination of glcFast  (DIRECT) and a clustering algorithm. 11.1.9
glcFast  Box-bounded global optimization, using only function values. Fortran MEX implementation of glcSolve . 11.1.11
glcSolve  Global mixed-integer nonlinear programming, using no derivatives. 11.1.12
infSolve  Constrained minimax optimization. Reformulates problem and calls any suitable nonlinear solver. 11.1.14
lpSimplex  Linear programming using a simplex algorithm. 11.1.16
MILPSOLVE  Mixed-integer programming using branch and bound. 11.1.18
L1Solve  Constrained L1 optimization. Reformulates problem and calls any suitable nonlinear solver. 11.1.17
mipSolve  Mixed-integer programming using a branch-and-bound algorithm. 11.1.19
nlpSolve  Constrained nonlinear minimization solver using a filter SQP algorithm. 11.1.21
pdco  Linearly constrained nonlinear minimization solver using a primal-dual barrier algorithm. 11.1.22
pdsco  Linearly constrained nonlinear minimization solver using a primal-dual barrier algorithm, for separable functions. 11.1.23
qpSolve  Non-convex quadratic programming. 11.1.24
slsSolve  Sparse constrained nonlinear least squares. Reformulates problem and calls any suitable sparse nonlinear solver. 11.1.25
sTrustr  Constrained convex optimization of partially separable functions, using a structural trust region algorithm. 11.1.26
ucSolve  Unconstrained optimization with simple bounds on the parameters. Implements Newton, quasi-Newton and conjugate-gradient methods. 11.1.27
 

Additional Fortran solvers in TOMLAB v4.0  are listed in Table 2. They are called using a set of MEX-file interfaces developed in TOMLAB .


Table 2: Additional solvers in TOMLAB Base Module v4.0 .
Function Description Reference
goalSolve  Solves sparse multi-objective goal attainment problems  
lsei  Dense constrained linear least squares  
qld  Convex quadratic programming  
Tfzero  Finding a zero to f(x) in an interval, x is one-dimensional. [73, 17]
Tlsqr  Sparse linear least squares. [63, 62, 71]
Tnnls  Nonnegative constrained linear least squares  
 

3.2.2  TOMLAB /BARNLP

The add-on toolbox TOMLAB /BARNLP solves large-scale nonlinear programming problems using a sparse primal-dual interior point algorithm, in conjunction with a filter for globalization. The solver package was developed in co-operation with Boeing Phantom Works. The TOMLAB /BARNLP package is described in a separate manual available at http://tomopt.com.

3.2.3  TOMLAB /CGO

The add-on toolbox TOMLAB /CGO solves costly global optimization problems. The solvers are listed in Table 3. They are written in a combination of Matlab and Fortran code, where the Fortran code is called using a set of MEX-file interfaces developed in TOMLAB .


Table 3: Additional solvers in TOMLAB /CGO.
Function Description Reference
rbfSolve  Costly constrained box-bounded optimization using a RBF algorithm. [9]
ego  Costly constrained box-bounded optimization using the Efficient Global Optimization (EGO) algorithm. [18]

3.2.4  TOMLAB /CONOPT

The add-on toolbox TOMLAB /CONOPT solves large-scale nonlinear programming problems with a feasible path GRG method . The solver package was developed in co-operation with Arki Consulting and Development A/S. The TOMLAB /CONOPT is described in a separate manual available at http://tomopt.com. There is also m-file help available.

3.2.5  TOMLAB /CPLEX

The add-on toolbox TOMLAB /CPLEX solves large-scale mixed-integer linear and quadratic programming problems. The solver package was developed in co-operation with ILOG S.A. The TOMLAB /CPLEX solver package and interface are described in a manual available at http://tomopt.com.

3.2.6  TOMLAB /KNITRO

The add-on toolbox TOMLAB /KNITRO solves large-scale nonlinear programming problems with interior (barrier) point trust-region methods. The solver package was developed in co-operation with Ziena Optimization Inc. The TOMLAB /KNITRO manual is available at http://tomopt.com.

3.2.7  TOMLAB /LGO

The add-on toolbox TOMLAB /LGO solves global nonlinear programming problems. The LGO solver package is developed by Pintér Consulting Services, Inc. [66] The TOMLAB /LGO manual is available at http://tomopt.com.

3.2.8  TOMLAB /MINLP

The add-on toolbox TOMLAB /MINLP  provides solvers for continuous and mixed-integer quadratic programming (qp,miqp) and continuous and mixed-integer nonlinear constrained optimization (con, minlp).

All four solvers, written by Roger Fletcher and Sven Leyffer, University of Dundee, Scotland, are available in sparse and dense versions. The solvers are listed in Table 4.

The TOMLAB /MINLP manual is available at http://tomopt.com.


Table 4: Solver routines in TOMLAB /MINLP .
Function Description Reference
bqpd  Quadratic programming using a null-space method. bqpdTL.m 
 
miqpBB  Mixed-integer quadratic programming using bqpd as subsolver. miqpBBTL.m 
 
filterSQP  Constrained nonlinear minimization using a Filtered Sequential QP method. filterSQPTL.m 
 
minlpBB  Constrained, mixed-integer nonlinear minimization using a branch-and-bound search scheme. filterSQP is used as NLP solver. minlpBBTL.m 

3.2.9  TOMLAB /MINOS

Another set of Fortran solvers were developed by the Stanford Optimization Laboratory (SOL). Table 5 lists the solvers included in TOMLAB /MINOS. The solvers are called using a set of MEX-file interfaces developed as part of TOMLAB . All functionality of the SOL solvers are available and changeable in the TOMLAB  framework in Matlab .


Table 5: The SOL optimization solvers in TOMLAB /MINOS .
Function Description Reference Page
MINOS 5.51  Sparse linear and nonlinear programming with linear and nonlinear constraints. [60]  
LP-MINOS  A special version of the MINOS 5.5  MEX-file interface for sparse linear programming. [60]  
QP-MINOS  A special version of the MINOS 5.5  MEX-file interface for sparse quadratic programming. [60]  
LPOPT 1.0-10  Dense linear programming. [33]  
QPOPT 1.0-10  Non-convex quadratic programming with dense constraint matrix and sparse or dense quadratic matrix. [33]  

3.2.10  TOMLAB /OQNLP

The add-on toolbox Tomlab /OQNLP uses a multistart heuristic algorithm to find global optima of smooth constrained nonlinear programs (NLPs) and mixed-integer nonlinear programs (MINLPs). The solver package was developed in co-operation with Optimal Methods Inc. The TOMLAB /OQNLP manual is available at http://tomopt.com.

3.2.11  TOMLAB /NLPQL

The add-on toolbox TOMLAB /NLPQL solves dense nonlinear programming problems, multi criteria optimization problems and nonlinear fitting problems. The solver package was developed in co-operation with Klaus Schittkowski. The TOMLAB /NLPQL manual is available at http://tomopt.com.

3.2.12  TOMLAB /NPSOL

The add-on toolbox TOMLAB /NPSOL is a sub package of TOMLAB /SOL. The package includes the MINOS solvers as well as NPSOL, LSSOL and NLSSOL. The TOMLAB /NPSOL manual is available at http://tomopt.com.

3.2.13  TOMLAB /PENBMI

The add-on toolbox TOMLAB /PENBMI solves linear semi-definite programming problems with linear and bilinear matrix inequalities. The solvers are listed in Table 6. They are written in a combination of Matlab and C code. The TOMLAB /PENBMI manual is available at http://tomopt.com.


Table 6: Additional solvers in TOMLAB /PENSDP.
Function Description Reference Page
penbmi  Sparse and dense linear semi-definite programming using a penalty algorithm.    
penfeas_bmi  Feasibility check of systems of linear matrix inequalities, using penbmi .    

3.2.14  TOMLAB /PENSDP

The add-on toolbox TOMLAB /PENSDP solves linear semi-definite programming problems with linear matrix inequalities. The solvers are listed in Table 7. They are written in a combination of Matlab and C code. The TOMLAB /PENSDP manual is available at http://tomopt.com.


Table 7: Additional solvers in Tomlab /PENSDP.
Function Description Reference Page
pensdp  Sparse and dense linear semi-definite programming using a penalty algorithm.    
penfeas_sdp  Feasibility check of systems of linear matrix inequalities, using pensdp .    

3.2.15  TOMLAB /SNOPT

The add-on toolbox TOMLAB /SNOPT is a sub package of TOMLAB /SOL. The package includes the MINOS solvers as well as SNOPT and SQOPT. The TOMLAB /SNOPT manual is available at http://tomopt.com.

3.2.16  TOMLAB /SOL

The extension toolbox TOMLAB  /SOL gives access to the complete set of Fortran solvers developed by the Stanford Systems Optimization Laboratory (SOL). These solvers are listed in Table 5 and 8.


Table 8: The optimization solvers in the TOMLAB /SOL toolbox.
Function Description Reference Page
NPSOL 5.02  Dense linear and nonlinear programming with linear and nonlinear constraints. [37]  
SNOPT 6.2-2  Large, sparse linear and nonlinear programming with linear and nonlinear constraints. [36, 34]  
SQOPT 6.2-2  Sparse convex quadratic programming. [35]  
NLSSOL 5.0-2  Constrained nonlinear least squares. NLSSOL is based on NPSOL. No reference except for general NPSOL reference. [37]  
LSSOL 1.05-4  Dense linear and quadratic programs (convex), and constrained linear least squares problems. [32]  

3.2.17  TOMLAB /SPRNLP

The add-on toolbox TOMLAB /SPRNLP solves large-scale nonlinear programming problems. SPRNLP is a state-of-the-art sequential quadratic programming (SQP) method, using an augmented Lagrangian merit function and safeguarded line search. The solver package was developed in co-operation with Boeing Phantom Works. The TOMLAB /SPRNLP package is described in a separate manual available at http://tomopt.com.

3.2.18  TOMLAB /XA

The add-on toolbox TOMLAB /XA solves large-scale linear, binary, integer and semi-continuous linear programming problems, as well as quadratic programming problems. The solver package was developed in co-operation with Sunset Software Technology. The TOMLAB /XA package is described in a separate manual available at http://tomopt.com.

3.2.19  TOMLAB /Xpress

The add-on toolbox TOMLAB /Xpress solves large-scale mixed-integer linear and quadratic programming problems. The solver package was developed in co-operation with Dash Optimization Ltd. The TOMLAB /Xpress solver package and interface are described in the html manual that comes with the installation package. There is also a TOMLAB /Xpress manual available at http://tomopt.com.

3.2.20  Finding Available Solvers

To get a list of all available solvers, including Fortran, C and Matlab  Optimization Toolbox solvers, for a certain solvType , call the routine SolverList  with solvType  as argument. solvType  should either be a string ('uc', 'con' etc.) or the corresponding solvType  number as given in Table 2.2. For example, if wanting a list of all available solvers of solvType  con, then 
SolverList('con')
gives the output 
>> SolverList('con');

Tomlab recommended choice for Constrained Nonlinear Programming (NLP)

npsol

Other solvers for NLP

   Licensed:

   nlpSolve
   conSolve
   sTrustr
   constr
   minos
   snopt
   fmincon
   filterSQP
   PDCO
   PDSCO


   Non-licensed:

   NONE

Solvers also handling NLP

   Licensed:

   glcSolve
   glcFast
   glcCluster
   rbfSolve
   minlpBB

   Non-licensed:

   NONE
SolverList  also returns two output arguments: all available solvers as a string matrix and a vector with the corresponding solvType  for each solver.

Note that solvers for a more general problem type may be used to solve the problem. In Table 9 an attempt has been made to classify these relations.


Table 9: The problem classes (probType) possible to solve with each type of solver (solvType) is marked with an x. When the solver is in theory possible to use, but from a practical point of view is probably not suitable, parenthesis are added (x).


  solvType
probType uc qp con ls lls cls mip lp glb glc
uc x   x           x (x)  
qp   x x             (x)  
con     x             (x)  
ls     x x   x       (x)  
lls   x x x x x       (x)  
cls     x     x       (x)  
mip             x     (x)  
lp   x x       x x   (x)  
glb     (x)           x x  
glc     (x)             x  
exp x   x (x)   x       (x)  

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