Install
Grab a binary from the table (for MATLAB, use the newest compatible version below):
<table>
<tr><th></th><th>Windows</th><th>Linux</th><th>Mac</th></tr>
<tr>
<th>Matlab</th>
<td><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-matlabR2016a-v3.5.0.zip">R2016a</a> or later,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-matlabR2014b-v3.5.0.zip">R2014b</a>,<br /> <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-matlabR2014a-v3.5.0.zip">R2014a</a>,<br /> <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-matlabR2013a-v3.5.0.zip">R2013a</a> or R2013b</td>
<td><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-matlabR2014b-v3.5.0.tar.gz">R2014b</a> or later,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-matlabR2014a-v3.5.0.tar.gz">R2014a</a></td>
<td><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-matlabR2015a-v3.5.0.tar.gz">R2015a</a> or later,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-matlabR2014b-v3.5.0.tar.gz">R2014b</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-matlabR2014a-v3.5.0.tar.gz">R2014a</a></td>
</tr>
<tr>
<th>Octave</th>
<td> 4.4.1 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-octave-4.4.1-w32-v3.5.0.zip">32bit</a> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-octave-4.4.1-w64-v3.5.0.zip">64bit</a>), <br /> 4.4.0 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-octave-4.4.0-w32-v3.5.0.zip">32bit</a> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-octave-4.4.0-w64-v3.5.0.zip">64bit</a>),
<br /> 5.1.0 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-octave-5.1.0-w32-v3.5.0.zip">32bit</a> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-octave-5.1.0-w64-v3.5.0.zip">64bit</a>)</td>
<td><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-octave-4.4.1-v3.5.0.tar.gz">4.4.1</a>, <br/><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-octave-4.2.2-v3.5.0.tar.gz">4.2.2</a>, <br/><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-octave-5.1.0-v3.5.0.tar.gz">5.1.0</a></td>
<td><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-octave-5.1.0-v3.5.0.tar.gz">5.1.0</a></td>
</tr>
<tr>
<th rowspan="2">Python</th>
<td>Py27 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py27-v3.5.0.zip">32bit</a><sup>*</sup> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py27-v3.5.0-64bit.zip">64bit</a><sup>*</sup>),<br /> Py35 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py35-v3.5.0.zip">32bit</a><sup>*</sup> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py35-v3.5.0-64bit.zip">64bit</a><sup>*</sup>),<br /> Py36 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py36-v3.5.0.zip">32bit</a><sup>*</sup> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py36-v3.5.0-64bit.zip">64bit</a><sup>*</sup>),<br /> Py37 (<a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py37-v3.5.0.zip">32bit</a><sup>*</sup> / <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-windows-py37-v3.5.0-64bit.zip">64bit</a><sup>*</sup>) </td>
<td><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-py27-v3.5.0-64bit.tar.gz">Py27</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-py35-v3.5.0-64bit.tar.gz">Py35</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-py36-v3.5.0-64bit.tar.gz">Py36</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-linux-py37-v3.5.0-64bit.tar.gz">Py37</a></td>
<td> <a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-py27-v3.5.0.tar.gz">Py27</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-py35-v3.5.0.tar.gz">Py35</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-py36-v3.5.0.tar.gz">Py36</a>,<br /><a href="https://github.com/casadi/casadi/releases/download/3.5.0/casadi-osx-py37-v3.5.0.tar.gz">Py37</a></td>
</tr>
<tr>
<td colspan="3">or just <code>pip install casadi</code> (needs <code>pip -V</code>>=8.1)</td>
</tr>
</table>
(<sup>*</sup>) Check your Python console if you need 32bit or 64bit - bitness should be printed at startup.
Unzip in your home directory and adapt the path:
<table>
<tr><th>Matlab/Octave</th><th>Python</th><tr>
<tr><td>
<pre>
<code>
addpath('<yourpath>/casadi-matlabR2014a-v3.4.5')
import casadi.*
x = MX.sym('x')
disp(jacobian(sin(x),x))
</code>
</pre>
</td><td>
<pre>
<code>
from sys import path
path.append(r"<yourpath>/casadi-py27-v3.4.5")
from casadi import *
x = MX.sym("x")
print(jacobian(sin(x),x))
</code>
</pre>
</td></tr>
</table>
Get started with the [example pack](https://github.com/casadi/casadi/releases/download/3.5.0/casadi-example_pack-v3.5.0.zip).
Troubleshooting
Release notes
CasADi Functions
* CasADi Functions can be serialized now ([308](https://github.com/casadi/casadi/issues/308)).
f.save('f.casadi') % Dump any CasADi Function to a file
f = Function.load('f.casadi') % Loads back in
This enables easy sharing of models/solver isntances beteen Matlab/Python/C++ cross-platform, and enables a form of [parallelization](https://web.casadi.org/blog/parfor/).
* You can now evaluate CasADi Functions from C [without requiring code-generation](https://github.com/casadi/casadi/blob/develop/docs/examples/cplusplus/c_api_usage.cpp). This makes it possible to embed CasADi computations in Fortran, Julia, FMI, ...
* All CasADi Functions support timing information now (`print_time`, default true for QP and NLP solvers). Use `record_time` to make timings available through `f.stats()` without printing them.
* `map` with reduce arguments now has an efficient implementation (no copying/repmat)
* Low-overhead Callback eval support was [changed](https://github.com/casadi/casadi/issues/2271) to `eval_buffer`
* `FunctionInternal::finalize` no longer takes options dict.
* Options `always_inline` and `never_inline` were added
* Options `is_diff_in` and `is_diff_out` were added
CasADi expressions
* **breaking**: `IM` type is removed from public API (was used to represent integer sparse matrices). Use `DM` instead.
* **breaking**: `linspace(0,1,3)` and `linspace(0.0,1,3)` now both return `[0 0.5 1]` instead of `[0 0 1]` for the former
* `MX` supports slicing with `MX` now (symbolic indexing).
* Issue [2364](https://github.com/casadi/casadi/issues/2364):
- **breaking**: `veccat` of an empty list now returns `0-by-1` instead of `0-by-0`.
- `jtimes` output dimensions have changed when any of the arguments is empty.
- NLP function object's 'lam_p' is now `0-by-1` in case of missing parameters.
Interpolation functionality
* Support for parametric (=changeable only, but not differentiable) grid and/or coefficients for linear/spline interpolation
* for `interpolant`, new constructors where added that takes dimensions instead of concrete vectors
* Support for symbolic (differentiable) grid and coefficients for linear interpolation (set `inline` option to true).
Python specific
* Overhead-less CasADi Function evaluation API [added](https://github.com/casadi/casadi/commit/3e9919e9195e8f057d09cd9bcc4f7fa59e6fc104#diff-ad1e1bf2c8d8bef2a0f3b3b85331ffd0R2491) through Python memoryviews
* Similar functionality in Callbacks
Matlab/Octave specific
* **breaking**: `a(:)=b` now behaves like Matlab builtin matrices when `a` is a CasADi matrix. Before, only the first column of `a` would be touched by this statement. ([2363](https://github.com/casadi/casadi/issues/2363))
* **breaking**: Fixed bug where `MX` constructor treated a numeric row vector as column vector. Now `size(MX(ones(1,4)))` returns `(1,4)` as expected. ([2366](https://github.com/casadi/casadi/issues/2366))
* Can now use spy directly on `DM`,`MX`,`SX`
Opti
* Opti supports conic problems now: `Opti('conic')`
* One can now easily obtain a parametric solution as a CasADi Function from an Opti instance:
opti = Opti()
x = opti.variable()
y = opti.variable()
p = opti.parameter()
opti.minimize(y**2+sin(x-y-p)**2)
opti.subject_to(x+y>=1)
opti.solver(nlpsolver,nlpsolver_options)
F = opti.to_function("F",[x,p,opti.lam_g],[x,y])
r = F(0,0.1,0)
* Using Opti together with `max_iter` is more natural now: use `solve_limited()` to avoid exceptions to be raised when iterations or time runs out. No need to try/catch.
Code-generation
* **breaking**: `external` now looks for a `.dylib` file, not `.so`
* **breaking**: Codegen API has changes related to thread-local memory:
* `void* mem` changed to `int mem`
* `alloc_mem`, `init_mem`, `free_mem` have been purged. `checkout` and `release` replace them.
int mem = checkout();
eval(arg, res, iw, w, mem);
release(mem);
* Codegen 'CODEGEN_PREFIX' has been renamed to 'CASADI_CODEGEN_PREFIX'
* QP solvers (QRQP, OSQP) and SqpMethod codegenerate now. This means that embedded MPC with CasADi is now more practical.
* Runge-Kutta and Collocation Integrator objects can be inlined into code-generatable MX Function with the 'simplify' option.
Solvers
* **breaking**: NLP solvers - `bound_consistency`, an option to post-process the primal and dual solution by projecting it on the bounds, introduced in 3.4, was changed to default `off`
* Sundials was patched to support multi-threading
* WORHP was bumped to v1.13
* SNOPT was bumped to v7.7
* SuperSCS (conic solver) was added
* OSQP (QP solver) was added
* CBC (LP solver) was added
* **breaking**: SQP Method
* `regularize_margin` option was added
* `regularize` (bool) option was removed. To get the effect of `regularize=true`, specify `convexify_strategy='regularize'`. Other strategies include clipping eigenvalues.
* line search was reverted from a custom designed thing, to standard textbook L1
* CPLEX and Gurobi got support for sos constraints
* Conic/qpsol interface extended for semidefinite programming and SOCP
* Solvers supporting SOCP: `Gurobi`, `SuperSCS`, `CPLEX`
* **breaking**: Newton Rootfinder now supports a `line_search` option (default true)
* Rootfinder now throws an exception by default ('error_on_fail' option true) when failing to converge
Convenience tools
* Debugging facilities:
- Function options `print_in`/`print_in` print inputs/outputs when numerically evaluating a function
- Function option `dump_in`/`dump_out` dumps to the file system
- Function option `dump` dumps the function itself (loadable with `Function.load`)
* `DM.from_file` and `DM.to_file` with a `MatrixMarket` and `txt` support
* Helping interaction with codegen with `main=true`: `Function.generate_in`/`Function.nz_from_in`/`Function.nz_to_in` to help creating input text files.
* `Function.convert_in`/`Function.convert_out` to switch between list and dictionary arguments/results
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