Implement a sparse version of the Jonker volgenant algorithm.

In all but extremely exotic use cases, tracking based on the LAP framework generates a highly sparse cost matrix. 

As soon as a particle is further away from its target than a typically small distance, we assign an infinite cost to the potential link between the two. This max distance is indeed typically small for the use cases covered by TrackMate: a typical microscope field can image several thousands of particles, by the frame interval must be chosen so that the distance travelled between frames is not too big compared to the size of particles (otherwise you run into sampling problem and then nothing can help you). 

So most of the costs in the cost matrix are infinite. But they still take a lot of memory. So much that in some cases, TrackMate would break when allocating the memory to store a gigantic cost matrix. 

This PR is the first step in making these use cases manageable by TrackMate. A sparse structure is used to represent costs that are non-infinite, and a dedicated version of the Jonker-Volgenant algorithm handles this representation. It saves a considerable amount of memory and push back the frontier of manageable problems.

This comes at a heavy cost on computation time. I copy here the results of the benchmark, comparing with @dscho non-sparse version of the algorithm. Benchmarks show that when increasing the density from 0.1% to 70%, the computation time increased by a factor ranging from 1.5 to 7 compared to the non-sparse version. For a given density, the comparison depends very weakly on the matrix size.

---------------
Varying density
---------------

500 x 500 cost matrix, 10 repetitions.
Density	TimeNotSparse(ms)	TimeSparse(ms)	Factor
0.0068	21.0			35.7		1.70
0.0073	5.6			8.2		1.46
0.0078	3.9			4.7		1.21
0.0083	5.5			8.3		1.51
0.0086	6.9			12.3		1.78
0.0093	4.7			6.9		1.47
0.0098	3.9			5.2		1.33
0.0102	5.8			10.7		1.84
0.0109	3.6			5.0		1.39
0.0115	4.9			8.1		1.65
0.0118	3.8			5.7		1.50
0.0127	6.1			12.1		1.98
0.0133	3.8			5.5		1.45
0.0140	4.9			9.2		1.88
0.0146	4.7			8.3		1.77
0.0154	6.1			13.1		2.15
0.0164	3.9			5.6		1.44
0.0170	6.3			14.2		2.25
0.0182	4.7			9.6		2.04
0.0193	6.5			16.2		2.49
0.0201	5.1			11.1		2.18
0.0208	7.4			19.3		2.61
0.0223	7.0			18.6		2.66
0.0234	6.2			15.7		2.53
0.0239	6.3			16.4		2.60
0.0257	4.3			9.2		2.14
0.0267	4.7			10.5		2.23
0.0282	5.9			16.1		2.73
0.0294	7.1			20.5		2.89
0.0307	4.7			11.3		2.40
0.0321	4.9			12.9		2.63
0.0338	7.1			22.0		3.10
0.0370	5.9			17.3		2.93
0.0379	5.2			14.9		2.87
0.0390	6.1			18.9		3.10
0.0425	7.1			24.7		3.48
0.0438	6.4			21.7		3.39
0.0467	4.9			15.0		3.06
0.0490	5.6			18.6		3.32
0.0511	5.9			20.1		3.41
0.0534	6.3			22.7		3.60
0.0567	5.0			16.8		3.36
0.0584	5.2			17.4		3.35
0.0622	3.9			10.8		2.77
0.0657	7.0			29.2		4.17
0.0679	6.2			24.4		3.94
0.0709	5.5			21.3		3.87
0.0745	6.5			26.2		4.03
0.0776	5.5			22.4		4.07
0.0816	5.0			19.2		3.84
0.0868	6.0			23.0		3.83
0.0888	4.9			18.0		3.67
0.0943	5.3			21.7		4.09
0.1004	7.0			34.4		4.91
0.1037	6.7			32.6		4.87
0.1085	5.2			21.0		4.04
0.1142	5.6			23.1		4.13
0.1184	6.1			26.1		4.28
0.1252	6.5			33.0		5.08
0.1330	6.9			32.5		4.71
0.1368	4.8			21.0		4.38
0.1412	5.8			26.8		4.62
0.1487	5.3			24.1		4.55
0.1574	4.8			21.3		4.44
0.1652	5.5			26.2		4.76
0.1730	5.1			24.9		4.88
0.1794	4.8			21.9		4.56
0.1876	5.9			31.6		5.36
0.1974	5.0			24.0		4.80
0.2050	4.9			23.4		4.78
0.2120	5.3			27.3		5.15
0.2207	5.1			24.2		4.75
0.2341	4.5			18.9		4.20
0.2382	5.9			32.7		5.54
0.2536	5.8			28.6		4.93
0.2643	6.3			34.5		5.48
0.2767	6.3			31.5		5.00
0.2850	5.6			25.7		4.59
0.2990	5.9			30.8		5.22
0.3107	4.7			20.5		4.36
0.3206	6.5			40.5		6.23
0.3372	5.8			33.3		5.74
0.3537	5.1			26.8		5.25
0.3690	5.4			28.1		5.20
0.3746	5.6			33.0		5.89
0.3935	4.7			24.3		5.17
0.4030	6.1			37.1		6.08
0.4228	6.2			36.3		5.85
0.4384	6.4			38.0		5.94
0.4552	5.4			32.5		6.02
0.4668	5.9			33.1		5.61
0.4848	6.6			37.7		5.71
0.4966	6.8			44.4		6.53
0.5222	7.0			46.6		6.66
0.5360	6.6			43.4		6.58
0.5512	6.4			42.0		6.56
0.5729	6.8			45.0		6.62
0.5816	5.5			33.9		6.16
0.6071	5.9			34.8		5.90
0.6235	7.6			54.3		7.14
------------
Varying size
------------

Size	TimeNotSparse(ms)	TimeSparse(ms)	Factor
 100	0.1			0.2		2.00
 118	0.2			0.4		2.00
 136	0.3			0.7		2.33
 154	0.4			1.2		3.00
 172	0.4			1.1		2.75
 190	0.6			1.1		1.83
 208	0.6			1.3		2.17
 226	0.8			2.2		2.75
 244	0.8			2.4		3.00
 262	1.4			4.5		3.21
 280	1.2			3.3		2.75
 298	1.5			4.5		3.00
 316	1.5			4.0		2.67
 334	1.3			3.2		2.46
 352	3.0			11.4		3.80
 370	2.3			7.2		3.13
 388	2.2			6.6		3.00
 406	3.6			14.2		3.94
 424	4.1			16.9		4.12
 442	4.0			15.0		3.75
 460	4.8			20.4		4.25
 478	4.1			14.0		3.41
 496	6.8			30.8		4.53
 514	6.8			29.6		4.35
 532	8.8			40.6		4.61
 550	9.8			42.6		4.35
 568	7.7			24.7		3.21
 586	8.7			29.5		3.39
 604	9.0			28.9		3.21
 622	9.9			31.6		3.19
 640	12.2			47.1		3.86
 658	14.7			61.4		4.18
 676	12.5			42.9		3.43
 694	15.7			64.0		4.08
 712	16.7			67.7		4.05
 730	15.9			60.0		3.77
 748	18.4			76.6		4.16
 766	18.6			70.8		3.81
 784	17.2			57.0		3.31
 802	22.3			84.7		3.80
 820	24.2			99.5		4.11
 838	22.3			79.1		3.55
 856	25.9			107.3	4.14
 874	24.6			93.5		3.80
 892	27.3			107.7	3.95
 910	32.2			141.8	4.40
 928	27.2			104.2	3.83
 946	23.5			68.1		2.90
 964	35.0			151.0	4.31
 982	36.5			146.7	4.02